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The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement


Routinely collected health data, obtained for administrative and clinical purposes without specific a priori research goals, are increasingly used for research. The rapid evolution and availability of these data have revealed issues not addressed by existing reporting guidelines, such as Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). The REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement was created to fill these gaps. RECORD was created as an extension to the STROBE statement to address reporting items specific to observational studies using routinely collected health data. RECORD consists of a checklist of 13 items related to the title, abstract, introduction, methods, results, and discussion section of articles, and other information required for inclusion in such research reports. This document contains the checklist and explanatory and elaboration information to enhance the use of the checklist. Examples of good reporting for each RECORD checklist item are also included herein. This document, as well as the accompanying website and message board (http://www.record-statement.org), will enhance the implementation and understanding of RECORD. Through implementation of RECORD, authors, journals editors, and peer reviewers can encourage transparency of research reporting.


Vyšlo v časopise: The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement. PLoS Med 12(10): e32767. doi:10.1371/journal.pmed.1001885
Kategorie: Guidelines and Guidance
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1001885

Souhrn

Routinely collected health data, obtained for administrative and clinical purposes without specific a priori research goals, are increasingly used for research. The rapid evolution and availability of these data have revealed issues not addressed by existing reporting guidelines, such as Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). The REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement was created to fill these gaps. RECORD was created as an extension to the STROBE statement to address reporting items specific to observational studies using routinely collected health data. RECORD consists of a checklist of 13 items related to the title, abstract, introduction, methods, results, and discussion section of articles, and other information required for inclusion in such research reports. This document contains the checklist and explanatory and elaboration information to enhance the use of the checklist. Examples of good reporting for each RECORD checklist item are also included herein. This document, as well as the accompanying website and message board (http://www.record-statement.org), will enhance the implementation and understanding of RECORD. Through implementation of RECORD, authors, journals editors, and peer reviewers can encourage transparency of research reporting.


Zdroje

1. Spasoff RA. Epidemiologic Methods for Health Policy. New York: Oxford University Press, Inc.; 1999.

2. Morrato EH, Elias M, Gericke CA. Using population-based routine data for evidence-based health policy decisions: lessons from three examples of setting and evaluating national health policy in Australia, the UK and the USA. Journal of public health (Oxford, England). 2007;29(4):463–71.

3. De Coster C, Quan H, Finlayson A, Gao M, Halfon P, Humphries KH, et al. Identifying priorities in methodological research using ICD-9-CM and ICD-10 administrative data: report from an international consortium. BMC Health Serv Res. 2006;6:77. 16776836

4. Hemkens LG, Benchimol EI, Langan SM, Briel M, Kasenda B, Januel JM, et al., editors. Reporting of studies using routinely collected health data: systematic literature analysis (oral abstract presentation). REWARD / EQUATOR Conference 2015; 2015 September 28–30; Edinburgh, UK.

5. Benchimol EI, Manuel DG, To T, Griffiths AM, Rabeneck L, Guttmann A. Development and use of reporting guidelines for assessing the quality of validation studies of health administrative data. J Clin Epidemiol. 2011;64(8):821–9. doi: 10.1016/j.jclinepi.2010.10.006 21194889

6. Herrett E, Thomas SL, Schoonen WM, Smeeth L, Hall AJ. Validation and validity of diagnoses in the General Practice Research Database: a systematic review. British journal of clinical pharmacology. 2010;69(1):4–14. doi: 10.1111/j.1365-2125.2009.03537.x 20078607

7. Rothman‬ KJ, Greenland S, Lash TL. Modern Epidemiology, 3rd edition. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.

8. Plint AC, Moher D, Morrison A, Schulz K, Altman DG, Hill C, et al. Does the CONSORT checklist improve the quality of reports of randomised controlled trials? A systematic review. The Medical journal of Australia. 2006;185(5):263–7. 16948622

9. Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network Library 2015 [cited 2015 Mar 7]. http://www.equator-network.org/library/.

10. Vandenbroucke JP, von Elm E, Altman DG, Gotzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLOS Medicine. 2007;4(10):e297. 17941715

11. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLOS Medicine. 2007;4(10):e296. 17941714

12. Sorensen AA, Wojahn RD, Manske MC, Calfee RP. Using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement to assess reporting of observational trials in hand surgery. The Journal of hand surgery. 2013;38(8):1584–9.e2. doi: 10.1016/j.jhsa.2013.05.008 23845586

13. Cobo E, Cortes J, Ribera JM, Cardellach F, Selva-O'Callaghan A, Kostov B, et al. Effect of using reporting guidelines during peer review on quality of final manuscripts submitted to a biomedical journal: masked randomised trial. BMJ. 2011;343:d6783. doi: 10.1136/bmj.d6783 22108262

14. Benchimol EI, Langan S, Guttmann A. Call to RECORD: the need for complete reporting of research using routinely collected health data. J Clin Epidemiol. 2013;66(7):703–5. doi: 10.1016/j.jclinepi.2012.09.006 23186992

15. Langan SM, Benchimol EI, Guttmann A, Moher D, Petersen I, Smeeth L, et al. Setting the RECORD straight: developing a guideline for the REporting of studies Conducted using Observational Routinely collected Data. Clin Epidemiol. 2013;5:29–31. doi: 10.2147/CLEP.S36885 23413321

16. Nicholls SG, Quach P, von Elm E, Guttmann A, Moher D, Petersen I, et al. The REporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) Statement: Methods for Arriving at Consensus and Developing Reporting Guidelines. PLOS One. 2015;10(5):e0125620. doi: 10.1371/journal.pone.0125620 25965407

17. Moher D, Schulz KF, Simera I, Altman DG. Guidance for developers of health research reporting guidelines. PLOS Medicine. 2010;7(2):e1000217. doi: 10.1371/journal.pmed.1000217 20169112

18. Glasziou P, Altman DG, Bossuyt P, Boutron I, Clarke M, Julious S, et al. Reducing waste from incomplete or unusable reports of biomedical research. Lancet. 2014;383(9913):267–76. doi: 10.1016/S0140-6736(13)62228-X 24411647

19. Blotiere PO, Weill A, Ricordeau P, Alla F, Allemand H. Perforations and haemorrhages after colonoscopy in 2010: a study based on comprehensive French health insurance data (SNIIRAM). Clin Res Hepatol Gastroenterol. 2014;38(1):112–7. doi: 10.1016/j.clinre.2013.10.005 24268997

20. Siregar S, Pouw ME, Moons KG, Versteegh MI, Bots ML, van der Graaf Y, et al. The Dutch hospital standardised mortality ratio (HSMR) method and cardiac surgery: benchmarking in a national cohort using hospital administration data versus a clinical database. Heart. 2014;100(9):702–10. doi: 10.1136/heartjnl-2013-304645 24334377

21. Price SD, Holman CD, Sanfilippo FM, Emery JD. Use of case-time-control design in pharmacovigilance applications: exploration with high-risk medications and unplanned hospital admissions in the Western Australian elderly. Pharmacoepidemiol Drug Saf. 2013;22(11):1159–70. doi: 10.1002/pds.3469 23797984

22. Gross CP, Andersen MS, Krumholz HM, McAvay GJ, Proctor D, Tinetti ME. Relation between Medicare screening reimbursement and stage at diagnosis for older patients with colon cancer. JAMA. 2006;296(23):2815–22. 17179458

23. Vandenbroucke JP. Observational research, randomised trials, and two views of medical science. PLOS Medicine. 2008;5(3):e67. doi: 10.1371/journal.pmed.0050067 18336067

24. Smith GD, Ebrahim S. Data dredging, bias, or confounding. BMJ. 2002;325(7378):1437–8. 12493654

25. Prokosch HU, Ganslandt T. Perspectives for medical informatics. Reusing the electronic medical record for clinical research. Methods Inf Med. 2009;48(1):38–44. 19151882

26. Benchimol EI, Manuel DG, Guttmann A, Nguyen GC, Mojaverian N, Quach P, et al. Changing Age Demographics of Inflammatory Bowel Disease in Ontario, Canada: A Population-based Cohort Study of Epidemiology Trends. Inflamm Bowel Dis. 2014;20(10):1761–9. doi: 10.1097/MIB.0000000000000103 25159453

27. Ducharme R, Benchimol EI, Deeks SL, Hawken S, Fergusson DA, Wilson K. Validation of diagnostic codes for intussusception and quantification of childhood intussusception incidence in ontario, Canada: a population-based study. J Pediatr. 2013;163(4):1073–9.e3. doi: 10.1016/j.jpeds.2013.05.034 23809052

28. Herrett E, Shah AD, Boggon R, Denaxas S, Smeeth L, van Staa T, et al. Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ. 2013;346:f2350. doi: 10.1136/bmj.f2350 23692896

29. van Herk-Sukel MP, van de Poll-Franse LV, Lemmens VE, Vreugdenhil G, Pruijt JF, Coebergh JW, et al. New opportunities for drug outcomes research in cancer patients: the linkage of the Eindhoven Cancer Registry and the PHARMO Record Linkage System. European journal of cancer (Oxford, England: 1990). 2010;46(2):395–404.

30. Fosbol EL, Granger CB, Peterson ED, Lin L, Lytle BL, Shofer FS, et al. Prehospital system delay in ST-segment elevation myocardial infarction care: a novel linkage of emergency medicine services and in hospital registry data. Am Heart J. 2013;165(3):363–70. doi: 10.1016/j.ahj.2012.11.003 23453105

31. Manuel DG, Rosella LC, Stukel TA. Importance of accurately identifying disease in studies using electronic health records. BMJ. 2010;341:c4226. doi: 10.1136/bmj.c4226 20724404

32. Narcolepsy in association with pandemic influenza vaccination (a multi-country European epidemiological investigation) Stockholm: ECDC. Stockholm: European Centre for Disease Prevention and Control., 2012 September. ISBN 978-92-9193-388-4. doi: 10.2900/63210

33. Lewis JD, Schinnar R, Bilker WB, Wang X, Strom BL. Validation studies of the health improvement network (THIN) database for pharmacoepidemiology research. Pharmacoepidemiol Drug Saf. 2007;16(4):393–401. 17066486

34. Sorensen HT, Sabroe S, Olsen J. A framework for evaluation of secondary data sources for epidemiological research. International journal of epidemiology. 1996;25(2):435–42. 9119571

35. Baron JA, Lu-Yao G, Barrett J, McLerran D, Fisher ES. Internal validation of Medicare claims data. Epidemiology. 1994;5(5):541–4. 7986870

36. Marston L, Carpenter JR, Walters KR, Morris RW, Nazareth I, White IR, et al. Smoker, ex-smoker or non-smoker? The validity of routinely recorded smoking status in UK primary care: a cross-sectional study. BMJ open. 2014;4(4):e004958. doi: 10.1136/bmjopen-2014-004958 24760355

37. Hardelid P, Dattani N, Gilbert R. Estimating the prevalence of chronic conditions in children who die in England, Scotland and Wales: a data linkage cohort study. BMJ open. 2014;4(8):e005331. doi: 10.1136/bmjopen-2014-005331 25085264

38. Murray J, Bottle A, Sharland M, Modi N, Aylin P, Majeed A, et al. Risk factors for hospital admission with RSV bronchiolitis in England: a population-based birth cohort study. PLOS One. 2014;9(2):e89186. doi: 10.1371/journal.pone.0089186 24586581

39. Berry JG, Hall M, Hall DE, Kuo DZ, Cohen E, Agrawal R, et al. Inpatient growth and resource use in 28 children's hospitals: a longitudinal, multi-institutional study. JAMA pediatrics. 2013;167(2):170–7. doi: 10.1001/jamapediatrics.2013.432 23266509

40. Shahian DM, Wolf RE, Iezzoni LI, Kirle L, Normand SL. Variability in the measurement of hospital-wide mortality rates. N Engl J Med. 2010;363(26):2530–9. doi: 10.1056/NEJMsa1006396 21175315

41. Springate DA, Kontopantelis E, Ashcroft DM, Olier I, Parisi R, Chamapiwa E, et al. ClinicalCodes: an online clinical codes repository to improve the validity and reproducibility of research using electronic medical records. PLOS One. 2014;9(6):e99825. doi: 10.1371/journal.pone.0099825 24941260

42. Dommett RM, Redaniel MT, Stevens MC, Hamilton W, Martin RM. Features of childhood cancer in primary care: a population-based nested case-control study. Br J Cancer. 2012;106(5):982–7. doi: 10.1038/bjc.2011.600 22240793

43. Tsang C, Bottle A, Majeed A, Aylin P. Adverse events recorded in English primary care: observational study using the General Practice Research Database. Br J Gen Pract. 2013;63(613):e534–42. doi: 10.3399/bjgp13X670660 23972194

44. Harron K, Goldstein H, Wade A, Muller-Pebody B, Parslow R, Gilbert R. Linkage, evaluation and analysis of national electronic healthcare data: application to providing enhanced blood-stream infection surveillance in paediatric intensive care. PLOS One. 2013;8(12):e85278. doi: 10.1371/journal.pone.0085278 24376874

45. Adams MM, Wilson HG, Casto DL, Berg CJ, McDermott JM, Gaudino JA, et al. Constructing reproductive histories by linking vital records. Am J Epidemiol. 1997;145(4):339–48. 9054238

46. Ford JB, Roberts CL, Taylor LK. Characteristics of unmatched maternal and baby records in linked birth records and hospital discharge data. Paediatr Perinat Epidemiol. 2006;20(4):329–37. 16879505

47. Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association: JAMIA. 2013;20(1):144–51. doi: 10.1136/amiajnl-2011-000681 22733976

48. Sandall J, Murrells T, Dodwell M, Gibson R, Bewley S, Coxon K, et al. The efficient use of the maternity workforce and the implications for safety and quality in maternity care: a population-based, cross-sectional study. Health Serv Deliv Res 2014;2(38).

49. Welch C, Petersen I, Walters K, Morris RW, Nazareth I, Kalaitzaki E, et al. Two-stage method to remove population- and individual-level outliers from longitudinal data in a primary care database. Pharmacoepidemiol Drug Saf. 2012;21(7):725–32. doi: 10.1002/pds.2270 22052713

50. Van den Broeck J, Cunningham SA, Eeckels R, Herbst K. Data cleaning: detecting, diagnosing, and editing data abnormalities. PLOS Medicine. 2005;2(10):e267. 16138788

51. Bohensky MA, Jolley D, Sundararajan V, Evans S, Pilcher DV, Scott I, et al. Data linkage: a powerful research tool with potential problems. BMC Health Serv Res. 2010;10:346. doi: 10.1186/1472-6963-10-346 21176171

52. Harron K, Wade A, Muller-Pebody B, Goldstein H, Gilbert R. Opening the black box of record linkage. J Epidemiol Community Health. 2012;66(12):1198. doi: 10.1136/jech-2012-201376 22705654

53. Lariscy JT. Differential record linkage by Hispanic ethnicity and age in linked mortality studies: implications for the epidemiologic paradox. J Aging Health. 2011;23(8):1263–84. doi: 10.1177/0898264311421369 21934120

54. Dinan MA, Curtis LH, Carpenter WR, Biddle AK, Abernethy AP, Patz EF Jr., et al. Variations in use of PET among Medicare beneficiaries with non-small cell lung cancer, 1998–2007. Radiology. 2013;267(3):807–17. doi: 10.1148/radiol.12120174 23418003

55. Horsfall L, Walters K, Petersen I. Identifying periods of acceptable computer usage in primary care research databases. Pharmacoepidemiol Drug Saf. 2013;22(1):64–9. doi: 10.1002/pds.3368 23124958

56. Gerber DE, Laccetti AL, Xuan L, Halm EA, Pruitt SL. Impact of prior cancer on eligibility for lung cancer clinical trials. J Natl Cancer Inst. 2014;106(11).

57. Carrara G, Scire CA, Zambon A, Cimmino MA, Cerra C, Caprioli M, et al. A validation study of a new classification algorithm to identify rheumatoid arthritis using administrative health databases: case-control and cohort diagnostic accuracy studies. Results from the RECord linkage On Rheumatic Diseases study of the Italian Society for Rheumatology. BMJ open. 2015;5(1):e006029. doi: 10.1136/bmjopen-2014-006029 25631308

58. Rait G, Walters K, Griffin M, Buszewicz M, Petersen I, Nazareth I. Recent trends in the incidence of recorded depression in primary care. Br J Psychiatry. 2009;195(6):520–4. doi: 10.1192/bjp.bp.108.058636 19949202

59. Wijlaars LP, Nazareth I, Petersen I. Trends in depression and antidepressant prescribing in children and adolescents: a cohort study in The Health Improvement Network (THIN). PLOS One. 2012;7(3):e33181. doi: 10.1371/journal.pone.0033181 22427983

60. Jeon CY, Pandol SJ, Wu B, Cook-Wiens G, Gottlieb RA, Merz NB, et al. The Association of Statin Use after Cancer Diagnosis with Survival in Pancreatic Cancer Patients: A SEER-Medicare Analysis. PLOS One. 2015;10(4):e0121783. doi: 10.1371/journal.pone.0121783 25830309

61. Pruitt Z, Pracht E. Upcoding emergency admissions for non-life-threatening injuries to children. The American journal of managed care. 2013;19(11):917–24. 24511988

62. McLintock K, Russell AM, Alderson SL, West R, House A, Westerman K, et al. The effects of financial incentives for case finding for depression in patients with diabetes and coronary heart disease: interrupted time series analysis. BMJ open. 2014;4(8):e005178. doi: 10.1136/bmjopen-2014-005178 25142262

63. Brunt CS. CPT fee differentials and visit upcoding under Medicare Part B. Health economics. 2011;20(7):831–41. doi: 10.1002/hec.1649 20681033

64. Walters K, Rait G, Griffin M, Buszewicz M, Nazareth I. Recent trends in the incidence of anxiety diagnoses and symptoms in primary care. PLOS One. 2012;7(8):e41670. doi: 10.1371/journal.pone.0041670 22870242

65. Nilson F, Bonander C, Andersson R. The effect of the transition from the ninth to the tenth revision of the International Classification of Diseases on external cause registration of injury morbidity in Sweden. Injury prevention: journal of the International Society for Child and Adolescent Injury Prevention 2015;21(3):189–94. doi: 10.1136/injuryprev-2014-041337 25344579

66. Jagai JS, Smith GS, Schmid JE, Wade TJ. Trends in gastroenteritis-associated mortality in the United States, 1985 inverted question mark2005: variations by ICD-9 and ICD-10 codes. BMC Gastroenterol. 2014;14(1):211. doi: 10.1186/s12876-014-0211-0 25492520

67. European Network of Centres for Pharmacoepidemiology and Pharmacovigilance Guide on Methodological Standards in Pharmacoepidemiology, 4.2.2.5. Unmeasured confounding [Mar 10]. London, UK: European Medicines Agency; 2015 [updated 2015; cited 2015 May 17]. http://www.encepp.eu/standards_and_guidances/methodologicalGuide4_2_2_5.shtml.

68. Toh S, Garcia Rodriguez LA, Hernan MA. Confounding adjustment via a semi-automated high-dimensional propensity score algorithm: an application to electronic medical records. Pharmacoepidemiol Drug Saf. 2011;20(8):849–57. doi: 10.1002/pds.2152 21717528

69. Stukel TA, Fisher ES, Wennberg DE, Alter DA, Gottlieb DJ, Vermeulen MJ. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. Jama. 2007;297(3):278–85. 17227979

70. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate behavioral research. 2011;46(3):399–424. doi: 10.1080/00273171.2011.568786 21818162

71. Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. doi: 10.1136/bmj.b2393 19564179

72. Freemantle N, Marston L, Walters K, Wood J, Reynolds MR, Petersen I. Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research. BMJ. 2013;347:f6409. doi: 10.1136/bmj.f6409 24217206

73. Marston L, Carpenter JR, Walters KR, Morris RW, Nazareth I, Petersen I. Issues in multiple imputation of missing data for large general practice clinical databases. Pharmacoepidemiol Drug Saf. 2010;19(6):618–26. doi: 10.1002/pds.1934 20306452

74. Benchimol EI, To T, Griffiths AM, Rabeneck L, Guttmann A. Outcomes of pediatric inflammatory bowel disease: socioeconomic status disparity in a universal-access healthcare system. J Pediatr. 2011;158(6):960–7.e1-4. doi: 10.1016/j.jpeds.2010.11.039 21227449

75. Nassar N, Dixon G, Bourke J, Bower C, Glasson E, de Klerk N, et al. Autism spectrum disorders in young children: effect of changes in diagnostic practices. International journal of epidemiology. 2009;38(5):1245–54. doi: 10.1093/ije/dyp260 19737795

76. Tan GH, Bhoo-Pathy N, Taib NA, See MH, Jamaris S, Yip CH. The Will Rogers phenomenon in the staging of breast cancer—does it matter? Cancer Epidemiol. 2015;39(1):115–7. doi: 10.1016/j.canep.2014.11.005 25475062

77. Taljaard M, Tuna M, Bennett C, Perez R, Rosella L, Tu JV, et al. Cardiovascular Disease Population Risk Tool (CVDPoRT): predictive algorithm for assessing CVD risk in the community setting. A study protocol. BMJ open. 2014;4(10):e006701. doi: 10.1136/bmjopen-2014-006701 25341454

78. Guttmann A, Schull MJ, Vermeulen MJ, Stukel TA. Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada. BMJ. 2011;342:d2983. doi: 10.1136/bmj.d2983 21632665

79. Nicol A, Caruso J, Archambault E. Open Data Access Policies and Strategies in the European Research Area and Beyond. Montreal, Canada: Science-Metrix Inc., 2013 Aug. Report No.

80. Fuller T, Pearson M, Peters J, Anderson R. What affects authors' and editors' use of reporting guidelines? Findings from an online survey and qualitative interviews. PLOS One. 2015;10(4):e0121585. doi: 10.1371/journal.pone.0121585 25875918

81. Turner L, Shamseer L, Altman DG, Weeks L, Peters J, Kober T, et al. Consolidated standards of reporting trials (CONSORT) and the completeness of reporting of randomised controlled trials (RCTs) published in medical journals. Cochrane Database Syst Rev. 2012;11:Mr000030.

82. Armstrong R, Waters E, Moore L, Riggs E, Cuervo LG, Lumbiganon P, et al. Improving the reporting of public health intervention research: advancing TREND and CONSORT. Journal of public health (Oxford, England). 2008;30(1):103–9.

83. Moher D, Cook DJ, Eastwood S, Olkin I, Rennie D, Stroup DF. Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement. Quality of Reporting of Meta-analyses. Lancet. 1999;354(9193):1896–900. doi: 10.1016/S0140-6736(99)04149-5 10584742

84. Prady SL, Richmond SJ, Morton VM, Macpherson H. A systematic evaluation of the impact of STRICTA and CONSORT recommendations on quality of reporting for acupuncture trials. PLOS One. 2008;3(2):e1577. doi: 10.1371/journal.pone.0001577 18270568

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