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Logo of injprevInjury PreventionCurrent TOCInstructions for authors
Inj Prev. Apr 2006; 12(2): 111–116.
PMCID: PMC2564435

Estimating injury severity using the Barell matrix



To determine whether the Barell matrix (Inj Prev 2002;8:91–6) could effectively categorize injuries by severity.


Injury diagnoses of cases in the 2002 US Nationwide Inpatient Sample were classified according to the Barell matrix. For each cell of the matrix, the authors used ICDMAP‐90 to determine the predominant Abbreviated Injury Score (AIS) and body region, and calculated the weighted proportion surviving (bPScell) among patients with any diagnosis in that cell. These findings were used to estimate maximum AIS (bAISmax), ISS (bISS), and the minimum or product of bPScell (bPSmin, bPSprod) for injured patients in the 1996–2000 US National Hospital Discharge Surveys. Case survival was determined for different scores, and outcome models using age, sex, comorbidity, mechanism, and bISS or bPSmin were compared to models using ISS calculated from ICDMAP‐90 (mISS) or using ICISS.


Case survival decreased with increasing bAISmax or bISS; survival was closely approximated by bPSmin, and also increased monotonically with bPSprod. Outcome models using bISS or bPSmin were similar to those using mISS or ICISS. An Abbreviated Barell Categorization, with only four groups, was also effective.


Barell matrix categorization of administrative data allows severity scoring similar to that obtainable with ICDMAP‐90 or ICISS.

Keywords: injury severity score, barell matrix, severity, mortality

Large administrative databases provide an inexpensive opportunity to analyze hospital outcomes and have been increasingly used for injury research. Specific injuries are usually described in these databases using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM), but clinically relevant aggregation of these thousands of diagnoses is required for meaningful analysis. The ability to stratify patients by injury severity is particularly important.

Sacco and colleagues1 tabulated the observed hospital mortality for patients with each diagnosis code, and assigned each patient an anatomic index (AI) equal to the maximum of the observed mortality probabilities for that patient's injury diagnoses. Levy and colleagues2 similarly tabulated the observed hospital survival for patients with each diagnosis code, and assigned each patient an estimated survival probability (ESP) equal to the product of the observed survival probabilities for that patient's injury diagnoses. A variation of the latter approach, the ICD‐9‐based Injury Severity Score (ICISS), was popularized years later by Osler and colleagues.3

The Abbreviated Injury Score (AIS)4 and Injury Severity Score (ISS)5 had been previously introduced for clinical studies, and have continued to be widely used. MacKenzie and colleagues6 have developed software (ICDMAP‐90, Tri‐Analytics, Baltimore, MD, USA) that maps ICD‐9‐CM diagnoses into AIS categories, thus allowing calculation of approximate AIS and/or ISS scores for administrative data.

More recently, Barell and an international group of colleagues7 developed a matrix of ICD‐9‐CM codes to classify injury diagnoses by type and anatomic region. Some of the original authors of the Barell matrix have subsequently demonstrated that “injury profiles” created by grouping cells of the matrix are associated with different mechanisms and outcomes.8 The purpose of our study was to use a large inpatient database to assign a probability of survival, AIS region, and AIS score to each cell of the Barell matrix. We wished to demonstrate that hospital outcomes in administrative data could then be predicted with reasonable accuracy by using this information to approximate the AI/ESP/ICISS or AIS/ISS methods.


Development of severity scores based upon the Barell matrix

After concluding the necessary data use agreement, Nationwide Inpatient Sample (NIS) data for 2002 were obtained from the US Agency for Healthcare Research and Quality.9 NIS contains inpatient data from about 1000 hospitals sampled to approximate a 20% stratified sample of US hospital discharges (potentially involving the same person more than once). Each record includes up to 15 ICD‐9‐CM diagnosis codes, age, sex, and hospital outcomes (including death and discharge to long term care). Each case is assigned a weight (Wi) essentially equal to the inverse of the probability that this case would be sampled, with further adjustments to account for missing data and other practical sampling problems. Summing the weights enables estimates that reflect the distribution of patients in US hospitals.

NIS cases were selected for this study if their principal ICD‐9‐CM diagnosis codes were in the range of injuries (800–959), excluding late effects of injury (905–909), foreign bodies (930–939), and complications (958). Cases received in transfer from other hospitals were included. Injury diagnoses from these cases were used to used to assign a predominant AIS score (bAIScell) and body region to each cell of the Barell matrix, as described in the online appendix (see http://www.injuryprevention.com/supplemental). The weighted proportion of patients who survived (bPScell) was calculated as

equation image

Any bPScell of 1 was replaced with a bPScell of .999.

For each patient in the NIS sample, the largest bAIScell (bAISmax) was determined overall and for each body region. We calculated bISS for each patient as the sum of squares of bAISmax for the three body regions with the greatest bAISmax,5 and categorized bISS as recommended by Copes et al.10 The minimum of the bPScell for a given case will be referred to as that patient's Barell PS minimum (bPSmin); the product of all bPScell for each case was calculated, and will be referred to as that patient's Barell PS product (bPSprod).

ICDMAP‐90 mapped each patient's ICD‐9‐CM diagnoses to an ISS body region and AIS score, and calculated the maximum AIS in each body region, maximum AIS overall (mAISmax), and ISS (mISS).5,6 Categorizations of AISmax and ISS derived from ICDMAP‐90 (mAISmax and mISS) were compared to those derived from the Barell matrix (bAISmax and bISS). Weighted hospital mortality for cases in the sample was calculated by bAISmax, bISS, bPSmin, and bPSprod.

Validation of severity scores using a separate database

National Hospital Discharge Survey (NHDS) data for 1996–2000 had been obtained from the US Centers for Disease Control and Prevention (CDC) on a compact disc and/or from the internet for a previous study.11 NHDS is a national probability sample of discharge data from acute care general hospitals in the United States conducted annually by the CDC.12 Like NIS, NHDS includes age, sex, up to seven ICD‐9‐CM diagnosis codes, hospital outcomes, and weights to allow estimates of national totals.

NHDS cases were selected using the inclusion criteria specified above. For each patient, bAISmax, bISS, bPSmin, and bPSprod were calculated using the Barell matrix along with the bAIScell and bPScell values that had been derived from the NIS data. As with the NIS cases, bAISmax and bISS were compared to the scores derived from ICDMAP‐90 (mAISmax and mISS). Weighted hospital mortality for NHDS cases was likewise calculated by bAISmax, bISS, bPSmin, and bPSprod.

Codes for the mechanism of injury (E‐codes) were classified as recommended by CDC.13 Diagnosis codes potentially relating to pre‐existing medical conditions were used to construct a comorbidity score as described by Charlson et al14 and Romano et al.15

For cases with complete data (including an E‐code), logistic regression was used to develop predictive models for hospital mortality based upon bISS or bPSmin, mechanism, comorbidity, age, and sex. Modeling techniques (using Stata Version 7.0 SE, College Station TX) included allowance for the weighted survey data.16 “Model based” evaluation11,17 included minimizing the Hosmer‐Lemeshow (H‐L) goodness‐of‐fit statistic and maximizing the area under a receiver operating characteristic curve.

For patients surviving to hospital discharge, similar logistic regression models were constructed and compared to predict the discharge destination of “long term care” (LTC). In addition, following the example of Mackenzie et al,18 multiple linear regression was used to predict the logarithm of hospital length of stay (LOS) for survivors.


Derivation of scores for Barell matrix cells

The 2002 NIS sample contained 306 303 cases meeting inclusion criteria, with weights ranging from 1.8 to 10.4 (mean 4.8). These cases thus represented about 1 470 000 (306 303×4.8) patients. Of these cases, 39.9% had more than one injury diagnosis, resulting in an average of 1.87 per case and a total diagnosis weight of about 2 749 000 (1 470 000×1.87). This weight was distributed as described in the online appendix (see http://www.injuryprevention.com/supplemental) to obtain the AIS scores given in table 11;; 99.0% of the weight was distributed to cells of the Barell matrix for which AIS regions and scores could be assigned objectively using ICDMAP‐90, and only 1.0% of the weight was distributed to cells requiring assignment using the authors' judgment. Each cell of the Barell matrix was thus associated with a predominant body region and bAIScell, none of which was greater than 4. Weighted estimates of the proportions of patients who survived for each cell (bPScell) ranged from .643 to .999 and are also shown in table 11..

Table thumbnail
Table 1 Barell matrix,7 with each cell (of those containing one or more diagnosis codes) assigned an AIS body region (A, abdomen; C, chest; E, extremity/pelvis; F, face; G, general; H, head/neck) bAIScell, and bPScell. )

Severity scores were easily calculated for individual patients using table 11.. For example, consider a hypothetical patient with diagnosis codes 800.00, 801.00, 852.20, and 821.01 (fractures of skull vault and skull base, subdural hematoma, unspecified loss of consciousness, and fracture of the femoral shaft). The Barell matrix7 assigns the first two diagnoses to cell 2A, the third to cell 1D, and the last to cell 29A. From table 11,, cell 2A is associated with the head/neck region, bAIS = 3, bPScell  = .992; cell 1D is associated with the head/neck region, bAIS = 4, bPScell  = .877; cell 29A is associated with the extremity region, bAIS = 3, bPScell  = .974. Thus, bAISmax in the head/neck region is 4, bAISmax in the extremity region is 3, and bISS is 4×4 + 3×3  = 25. We see that bPSmin is .877, and calculate bPSprod as .992×.877×.974  = .847.

Evaluation of results in the development (NIS) sample

Calculated bISS ranged from 1 to 33. For lesser degrees of injury, categorization of individual NIS cases by bISS agreed closely with the mISS categorizations obtained using ICDMAP‐90 (table 22);); for ISS above 15, the agreement was not as close but the great majority of cases were in the same or an adjacent category.

Table thumbnail
Table 2 Percentages of NIS (development) cases and NHDS (validation) cases (shown in italics) with the given ISS scores derived using the Barell matrix (bISS) compared to their ISS scores derived using ICDMAP‐90 (mISS)

Hospital mortality increased monotonically with increasing bAISmax or categorized bISS (fig 11)) and decreased monotonically with increasing bPSmin or bPSprod (fig 22).). There were few cases with bPSmin<.80 or bPSprod<.80; for the others, the expected line of (1 ‐ observed mortality) was more closely approximated by bPSmin than by bPSprod.

figure ip10058.f1
Figure 1 Hospital mortality for NIS (development) and NHDS (validation) samples, categorized by Barell Injury Severity Score (bISS) and Barell maximum AIS (bAISmax).
figure ip10058.f2
Figure 2 Hospital mortality for NIS (development) and NHDS (validation) samples, categorized by the minimum or product (bPSmin or bPSprod) of the estimated hospital survival for all cells of the Barell matrix containing one or more of the patient's ...

To evaluate the effect on mortality of bAIS in different body regions, we determined the weighted proportion of patients who died for each bAISmax in each body region, where the patients had no bAIScell in any other region equal or greater (table 33).). Mortality was not linearly or even monotonically related to bAIS and varied by body region: a bAIS of 4 in the head/neck region was associated with the highest mortality (12.5%), followed by a bAIS of 3 in the general or abdominal regions (11.0% and 8.8%, respectively); a bAIS of 3 or 4 in the chest or extremity regions was associated with significantly lower mortality, and a bAIS of 3 in the head/neck region still lower. Of NIS cases with overall bAISmax = 4, 97% had a bAIS of 4 in the head/neck region.

Table thumbnail
Table 3 Proportions of NIS (development) cases and NHDS (validation) cases (shown in italics) who died with the given regional bAISmax, and no bAIScell in any other body region that was equal or greater

Evaluation of results in the validation (NHDS) sample

The 1996–2000 NHDS sample contained 62 248 cases meeting inclusion criteria, with weights ranging from 6 to 3507 (mean 126). Of these cases, 36.7% had more than one injury diagnosis, resulting in an average of 1.73 per case.

Hospital mortality in the NHDS sample increased monotonically with increasing categories of bAISmax or bISS (fig 11)) and decreased monotonically with increasing categories of bPSmin or bPSprod (fig 22).). There were few cases with bPSmin<.80 or bPSprod<.80; for the others, the expected line of (1 ‐ observed mortality) was again more closely approximated by bPSmin than by bPSprod.

Calculated bISS ranged from 1 to 36. Categorization of individual NHDS cases by bISS also corresponded well to the mISS categorizations obtained using ICDMAP‐90 (table 22),), although not quite as closely as the development cases from NIS. For cases with bISS 25 or greater, 98.0% had a bAISmax of 4 in the head/neck region; for those with bISS 16–24, 91.5% had a bAISmax of 4 in the head/neck region. The effects of bAISmax in different body regions were similar to those seen with the NIS development sample (table 33).

Multivariate models using either bISS or bPSmin along with other factors (see online appendix at http://www.injuryprevention.com/supplemental) gave similar results to those reported previously using the same database and same data categorizations but grading anatomic severity using mISS or ICISS.11 Increased bISS or decreased bPSmin increased mortality, LTC, and LOS, as did age and comorbidity. Sex affected LTC and LOS but not mortality. Age was much more important than injury severity in determining LTC, and all these covariates had less effect on LOS than on other outcomes. Penetrating, burn, or vehicle mechanisms tended to increase mortality and decrease LTC, but these effects were not always significant.

Abbreviated Barell Categorization

Inspection of the variable effects of regional bAIS on mortality, using either NIS or NHDS data (table 33),), showed that the calculated bISS depends mostly on the presence or absence of a diagnosis resulting in bAIS = 4 in the head/neck region, and to a lesser degree on the presence or absence of a diagnosis resulting in bAIS[gt-or-equal, slanted]3 in any other region. We therefore defined an Abbreviated Barell Categorization (ABC), as:

  • 0 for head/neck bAIS<4 with all other bAIS<3;
  • 1 for head/neck bAIS<4 with another regional bAIS[gt-or-equal, slanted]3;
  • 2 for head/neck bAIS = 4 with all other bAIS<3; and
  • 3 for head/neck bAIS = 4 with another regional bAIS[gt-or-equal, slanted]3.

To a large extent, ABC = 0 corresponds to patients with bISS less than 9, ABC = 1 corresponds to patients with bISS 9–15, ABC = 2 corresponds to patients with bISS 16–24, and ABC = 3 corresponds to patients with bISS 25 or more. ISS scores for these patients obtained using ICDMAP‐90 (mISS) are similar, and inpatient mortality is similar to that seen with categorized bISS or mISS (table 44).). When ABC categories were substituted for the corresponding bISS categories in regression models using NHDS data, similar results were obtained (see online appendix).

Table thumbnail
Table 4 Abbreviated Barell Categorization (ABC), showing distributions of bISS and mISS, along with hospital mortality estimated from the development sample (NIS) and the validation sample (NHDS, shown in italics)


Much has been written about injury severity scoring, and many scores have been proposed.19,20 However, there has been no agreement on a standard methodology, especially for administrative data.21 The AIS is now owned by the American Association for Automotive Medicine, which has updated the methods and changed the body regions from those traditionally used to calculate the ISS. ICDMAP‐90 also remains proprietary, and has not been updated to include ICD‐9‐CM diagnoses added since 1990. The developers of ICISS originally provided a table of survival statistics grouped by ICD‐9‐CM codes through an internet site,3 which is no longer available.

The development of the Barell matrix under the direction of an International Collaborative Effort (ICE) on Injury Statistics,7 and its placement in the public domain, therefore offer a new opportunity to make a severity scoring system available at no cost for administrative studies anywhere that data are coded using ICD‐9‐CM (or its successors). The original authors of the Barell matrix clearly did not intend it to be used for severity scoring, and the adaptations we describe may at first appear to be oversimplified. However, we believe our results show that these methods are adequate for most purposes, such as selecting the most serious of several injuries, stratifying patients for outcome studies, or comparing statistics among different countries or regions.

Administrative data are usually derived from records intended to justify hospital claims for reimbursement. Accordingly, any use of their diagnosis codes for other purposes must be undertaken with caution. Among other concerns, discharge data reflect an episode of care rather than an incident case. Patients transferred from another hospital may be counted twice if they were originally inpatients in another sampled hospital, but excluding such patients (assuming they can be identified) would falsely eliminate patients transferred from another hospital's outpatient (emergency) department as well as those transferred from hospitals outside the sample. Patients readmitted for complications or other follow up care may also be falsely included as incident cases.

In addition, outcome analysis using hospital records by definition ignores the large proportion of injury deaths that occur before hospital admission as well as the large proportion of deaths in older patients that occur after hospital discharge.22 A survival probability calculated from an individual diagnosis code may also give a false sense of accuracy if it is based on a small number of cases or identifies an injury (for example, hip fracture) for which the apparent mortality reflects host factors or the timing of discharge to LTC facilities more than anatomic damage.

If hospital survival given hospital admission is the only outcome of interest, a PSmin/PSprod/AI/ESP/ICISS type of statistic may logically be used, although there is no longer any standard reference database. Any rational subgrouping of the thousands of possible injury diagnoses would probably have some value, but it is appealing to build upon the longstanding efforts of the ICE to distinguish clinically meaningful anatomic categories. In their evaluation of the ICISS method, Kilgo and colleagues23 found that the survival probability for the single worst injury (like the PSmin or AI) predicted survival for a given patient better than a multiplicative model (like the PSprod, ESP, or ICISS). The present study confirms that a multiplicative model tends to overpredict mortality.

Key points

  • Although the Barell matrix was not originally intended as a method of injury severity scoring, the authors adapt it for this purpose, using the US Nationwide Inpatient Sample as a reference database.
  • Table 11 in this article associates each cell of the Barell matrix with an Abbreviated Injury Score and the proportion of patients surviving with a diagnosis in this cell.
  • The Barell matrix, along with table 11,, can be used to calculate approximate regional or overall maximum AIS, ISS, or ICISS‐type severity scores for case records in administrative datasets.
  • The authors also propose an Abbreviated Barell Categorization as a simple way to approximate ISS in administrative data.
  • The usefulness of these methods is demonstrated by accurately predicting patient outcomes in another database (the US National Hospital Discharge Survey).

Further grouping of diagnoses using AIS incorporates prior expert opinion ranking severity ordinally by the amount of energy transferred, which may account for the continued popularity of ICDMAP‐90. Our findings suggest that the ICD‐9‐CM diagnoses it is unable to map are mostly minor injuries (table 22).). However, previous evaluation of the relation of mortality to AIS (as assigned by ICDMAP‐90) has shown that this effect is non‐linear and varies by region;11 this variability is confirmed in the present study ((tablestables 2 and 33).). Gennarelli and colleagues24 have emphasized that neurological injuries are the most important contributors to hospital trauma mortality, and our analysis of bAIS also shows the dominant effect of severe injuries in the head/neck region.

For most applications, a theoretically continuous scale of injury severity is no more useful than classification into a few ranked strata.10 When dealing with large numbers of patients and data of marginal descriptive power, a simpler scale may be sufficient and preferable. Our table 11 uses the NIS as a large reference database, and may be applied to any large set of administrative data for which the diagnoses have been categorized using the Barell matrix. This approach is easier than using ICDMAP‐90 or an ICISS‐type score that assigns a score to every ICD‐9‐CM diagnosis code. The Abbreviated Barell Categorization we describe (table 44)) is still easier and may be sufficient for many studies using administrative data.

Methods for predicting hospital mortality directly from ICD codes, as well as those incorporating AIS, can be adapted for use with the Barell matrix, with the advantages of simplicity, free access, and an internationally accepted nomenclature. Considering that any scoring system based on billing codes will have limited accuracy, these methods appear to stratify anatomic diagnoses adequately for most outcome studies, especially when used along with measures of comorbidity (including age) and other factors available in administrative data.


This work was funded in part by a grant #R49/CCR115279‐04 from the US National Center for Injury Prevention and Control. Its contents reflect the views of the authors, but not necessarily the NCIPC or CDC. The authors appreciate the many suggestions offered by anonymous reviewers.


ABC - Abbreviated Barell Categorization

AI - anatomic index

AIS - Abbreviated Injury Score

bAIScell - predominant AIS for a cell of the Barell matrix

bAISmax - maximum AIS for a patient estimated using Barell matrix

bISS - ISS for a patient estimated using using Barell matrix

bPScell - proportion of patients surviving with a diagnosis in a cell

bPSmin - minimum bPScell for a patient

bPSprod - product of bPScell for a patient

CDC - Centers for Disease Control and Prevention

ESP - estimated survival probability

ICD‐9‐CM - International Classification of Diseases, Ninth Revision, Clinical Modification

ICE - International Collaborative Effort on Injury Statistics

ICISS - ICD‐9‐based Injury Severity Score

ISS - Injury Severity Score

LOS - length of stay

LTC - long term care

mAISmax - maximum AIS for a patient estimated using ICDMAP‐90

mISS - ISS for a patient estimated using using ICDMAP‐90

NHDS - National Hospital Discharge Survey

NIS - Nationwide Inpatient Sample


Competing interests none.


1. Champion H R, Sacco W J, Lepper R L. et al An anatomic index of injury severity. J Trauma 1980. 20197–202.202 [PubMed]
2. Levy P S, Mullner R, Goldberg J. et al The estimated survival probability index of trauma severity. Health Serv Res 1978. 1328–35.35 [PMC free article] [PubMed]
3. Osler T, Rutledge R, Deis J. et al ICISS: An International Classification of Disease‐9 based injury severity score. J Trauma 1996. 41380–386.386 [PubMed]
4. AMA Committee on Medical Aspects of Automotive Safety Rating the severity of tissue damage. I. The abbreviated scale. JAMA 1971. 215277–280.280 [PubMed]
5. Baker S P, O'Neill B, Haddon W J. et al The injury severity score: A method for describing patients with multiple injuries and evaluating emergency care. J Trauma 1974. 14187–196.196 [PubMed]
6. MacKenzie E J, Steinwachs D M, Shankar B. Classifying trauma severity based on hospital discharge diagnoses. Validation of an ICD‐9CM to AIS‐85 conversion table. Med Care 1989. 27412–422.422 [PubMed]
7. Barell V, Aharonson‐Daniel L, Fingerhut L A. et al An introduction to the Barell body region by nature of injury diagnosis matrix. Inj Prev 2002. 891–96.96 [PMC free article] [PubMed]
8. Aharonson‐Daniel L, Boyko V, Ziv A. et al A new approach to the analysis of multiple injuries using data from a national trauma registry. Inj Prev 2003. 9156–162.162 [PMC free article] [PubMed]
9. US Agency for Healthcare Research and Quality National Inpatient Sample. Available at www.hcup‐us.ahrq.gov/nisoverview.jsp (accessed January 2006)
10. Copes W S, Champion H R, Sacco W J. et al The Injury Severity Score revisited. J Trauma 1988. 2869–77.77 [PubMed]
11. Clark D E, Winchell R J. Risk adjustment for injured patients using administrative data. J Trauma 2004. 57130–140.140 [PubMed]
12. US Centers for Disease Control and Prevention National Hospital Discharge Survey. Available at www.cdc.gov/nchs/about/major/hdasd/nhds.htm (accessed January 2006)
13. Recommended framework for presenting injury mortality data MMWR Morb Mortal Wkly Rep. 1997;46:1–30. [PubMed]
14. Charlson M E, Pompei P, Ales K L. et al A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 1987. 40373–383.383 [PubMed]
15. Romano P S, Roos L L, Jollis J G. Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative data: Differing perspectives. J Clin Epidemiol 1993. 461075–1079.1079 [PubMed]
16. Levy P S, Lemeshow S. Sampling of populations: methods and applications. New York: John Wiley & Sons, 1999
17. Hosmer D W, Jr, Lemeshow S. Applied logistic regression, 2nd edition. New York: John Wiley & Sons, 2000
18. MacKenzie E J, Morris J A, Jr, Edelstein S L. Effect of pre‐existing disease on length of hospital stay in trauma patients. J Trauma 1989. 29757–764.764 [PubMed]
19. Osler T. Injury severity scoring: perspectives in development and future directions. Am J Surg 1993. 16543S–51S.51S [PubMed]
20. Senkowski C K, McKenney M G. Trauma scoring systems: A review. J Am Coll Surg 1999. 189491–503.503 [PubMed]
21. Expert Group on Injury Severity Measurement, National Center for Health Statistics Discussion document on injury severity measurement in administrative datasets. Available at www.cdc.gov/nchs/injury.htm (accessed January 2006)
22. Mullins R J, Mann N C, Hedges J R. et al Adequacy of hospital discharge status as a measure of outcome among injured patients. JAMA 1998. 2791727–1731.1731 [PubMed]
23. Kilgo P D, Osler T M, Meredith W. The worst injury predicts mortality outcome the best: Rethinking the role of multiple injuries in trauma outcome scoring. J Trauma 2003. 55599–606.606 [PubMed]
24. Gennarelli T A, Champion H R, Copes W S. et al Comparison of mortality, morbidity, and severity of 59,713 head injured patients with 114,447 patients with extracranial injuries. J Trauma 1994. 37962–968.968 [PubMed]

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