Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 29

1.
2.

Calcium channel blockers and cancer: a risk analysis using the UK Clinical Practice Research Datalink (CPRD).

Grimaldi-Bensouda L, Klungel O, Kurz X, de Groot MC, Maciel Afonso AS, de Bruin ML, Reynolds R, Rossignol M.

BMJ Open. 2016 Jan 8;6(1):e009147. doi: 10.1136/bmjopen-2015-009147.

3.

Impact of primary care nursing workforce characteristics on the control of high-blood pressure: a multilevel analysis.

Parro-Moreno A, Serrano-Gallardo P, Díaz-Holgado A, Aréjula-Torres JL, Abraira V, Santiago-Pérez IM, Morales-Asencio JM.

BMJ Open. 2015 Dec 7;5(12):e009126. doi: 10.1136/bmjopen-2015-009126.

4.

Building a self-measuring healthcare system with computable metrics, data fusion, and substitutable apps.

Mandl KD, Mandel JC.

BMJ Outcomes. 2015 Apr;2015(1):6-13. No abstract available.

5.

Recommendations for the use of operational electronic health record data in comparative effectiveness research.

Hersh WR, Cimino J, Payne PR, Embi P, Logan J, Weiner M, Bernstam EV, Lehmann H, Hripcsak G, Hartzog T, Saltz J.

EGEMS (Wash DC). 2013 Oct 8;1(1):1018. doi: 10.13063/2327-9214.1018.

6.

Considerations for using research data to verify clinical data accuracy.

Fort D, Weng C, Bakken S, Wilcox AB.

AMIA Jt Summits Transl Sci Proc. 2014 Apr 7;2014:211-7.

7.

Corticosteroid Treatments in Males With Duchenne Muscular Dystrophy: Treatment Duration and Time to Loss of Ambulation.

Kim S, Campbell KA, Fox DJ, Matthews DJ, Valdez R; MD STARnet..

J Child Neurol. 2015 Sep;30(10):1275-80. doi: 10.1177/0883073814558120.

8.

Toward a science of learning systems: a research agenda for the high-functioning Learning Health System.

Friedman C, Rubin J, Brown J, Buntin M, Corn M, Etheredge L, Gunter C, Musen M, Platt R, Stead W, Sullivan K, Van Houweling D.

J Am Med Inform Assoc. 2015 Jan;22(1):43-50. doi: 10.1136/amiajnl-2014-002977.

9.

Body characteristics, [corrected] dietary protein and body weight regulation. Reconciling conflicting results from intervention and observational studies?

Ankarfeldt MZ, Ängquist L, Stocks T, Jakobsen MU, Overvad K, Halkjær J, Saris WH, Astrup A, Sørensen TI.

PLoS One. 2014 Jul 3;9(7):e101134. doi: 10.1371/journal.pone.0101134. Erratum in: PLoS One. 2014;9(8):e106157.

10.

Estimating causal effects in observational studies using Electronic Health Data: Challenges and (some) solutions.

Stuart EA, DuGoff E, Abrams M, Salkever D, Steinwachs D.

EGEMS (Wash DC). 2013;1(3). doi: 10.13063/2327-9214.1038.

11.

Can analyses of electronic patient records be independently and externally validated? The effect of statins on the mortality of patients with ischaemic heart disease: a cohort study with nested case-control analysis.

Reeves D, Springate DA, Ashcroft DM, Ryan R, Doran T, Morris R, Olier I, Kontopantelis E.

BMJ Open. 2014 Apr 23;4(4):e004952. doi: 10.1136/bmjopen-2014-004952.

12.

A compilation of research working groups on drug utilisation across Europe.

Sabaté M, Pacheco JF, Ballarín E, Ferrer P, Petri H, Hasford J, Schoonen MW, Rottenkolber M, Fortuny J, Laporte JR, Ibáñez L; PROTECT Work Package 2..

BMC Res Notes. 2014 Mar 13;7:143. doi: 10.1186/1756-0500-7-143.

13.

Individual and composite study endpoints: separating the wheat from the chaff.

Goldberg R, Gore JM, Barton B, Gurwitz J.

Am J Med. 2014 May;127(5):379-84. doi: 10.1016/j.amjmed.2014.01.011. Review.

14.

Bias and sensitivity analysis when estimating treatment effects from the cox model with omitted covariates.

Lin NX, Logan S, Henley WE.

Biometrics. 2013 Dec;69(4):850-60. doi: 10.1111/biom.12096.

15.

Don't take your EHR to heaven, donate it to science: legal and research policies for EHR post mortem.

Huser V, Cimino JJ.

J Am Med Inform Assoc. 2014 Jan-Feb;21(1):8-12. doi: 10.1136/amiajnl-2013-002061.

16.

Applying active learning to high-throughput phenotyping algorithms for electronic health records data.

Chen Y, Carroll RJ, Hinz ER, Shah A, Eyler AE, Denny JC, Xu H.

J Am Med Inform Assoc. 2013 Dec;20(e2):e253-9. doi: 10.1136/amiajnl-2013-001945.

17.

Caveats for the use of operational electronic health record data in comparative effectiveness research.

Hersh WR, Weiner MG, Embi PJ, Logan JR, Payne PR, Bernstam EV, Lehmann HP, Hripcsak G, Hartzog TH, Cimino JJ, Saltz JH.

Med Care. 2013 Aug;51(8 Suppl 3):S30-7. doi: 10.1097/MLR.0b013e31829b1dbd.

18.

Rapid, responsive, relevant (R3) research: a call for a rapid learning health research enterprise.

Riley WT, Glasgow RE, Etheredge L, Abernethy AP.

Clin Transl Med. 2013 May 10;2(1):10. doi: 10.1186/2001-1326-2-10.

19.

Reusing electronic patient data for dental clinical research: a review of current status.

Song M, Liu K, Abromitis R, Schleyer TL.

J Dent. 2013 Dec;41(12):1148-63. doi: 10.1016/j.jdent.2013.04.006. Review.

20.

Feasibility of studying brain morphology in major depressive disorder with structural magnetic resonance imaging and clinical data from the electronic medical record: a pilot study.

Hoogenboom WS, Perlis RH, Smoller JW, Zeng-Treitler Q, Gainer VS, Murphy SN, Churchill SE, Kohane IS, Shenton ME, Iosifescu DV.

Psychiatry Res. 2013 Mar 30;211(3):202-13. doi: 10.1016/j.pscychresns.2012.07.007.

Supplemental Content

Support Center