Computer-Aided Systematic Review Screening Comes of Age
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Résumé
Editorials1 August 2017Computer-Aided Systematic Review Screening Comes of AgeBrian J. Hemens, BScPhm, MSc, RPh and Alfonso Iorio, MD, PhDBrian J. Hemens, BScPhm, MSc, RPhFrom McMaster University, Hamilton, Ontario, Canada.Search for more papers by this author and Alfonso Iorio, MD, PhDFrom McMaster University, Hamilton, Ontario, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M17-1295 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Publication of the 2017 update of the clinical practice guideline on the treatment of low bone density or osteoporosis to prevent fractures from the American College of Physicians (1) marks an important advancement in systematic review methodology. In addition to altering advice that will improve patient care, this update is based on new evidence found by training a computer to automatically identify relevant references.Shekelle and colleagues (2) describe important refinements to machine-learning software designed to reduce the need for human activity to identify relevant studies for a systematic review update. Those who conduct systematic reviews know that as the ...References1. Qaseem A, Forciea MA, McLean RM, Denberg TD; Clinical Guidelines Committee of the American College of Physicians. Treatment of low bone density or osteoporosis to prevent fractures in men and women: a clinical practice guideline update from the American College of Physicians. Ann Intern Med. 2017;166:818-39. [PMID: 28492856]. doi:10.7326/M15-1361 LinkGoogle Scholar2. Shekelle PG, Shetty K, Newberry S, Maglione M, Motala A. Machine learning versus standard techniques for updating searches for systematic reviews: a diagnostic accuracy study [Letter]. Ann Intern Med. 2017;167:213-5. doi:10.7326/L17-0124 LinkGoogle Scholar3. Shojania KG, Sampson M, Ansari MT, Ji J, Doucette S, Moher D. How quickly do systematic reviews go out of date? A survival analysis. Ann Intern Med. 2007;147:224-33. [PMID: 17638714] LinkGoogle Scholar4. Garner P, Hopewell S, Chandler J, MacLehose H, Schünemann HJ, Akl EA, et al; Panel for Updating Guidance for Systematic Reviews (PUGs). When and how to update systematic reviews: consensus and checklist. BMJ. 2016;354:i3507. [PMID: 27443385] doi:10.1136/bmj.i3507 CrossrefMedlineGoogle Scholar5. Wilczynski NL, McKibbon KA, Haynes RB. Enhancing retrieval of best evidence for health care from bibliographic databases: calibration of the hand search of the literature. Stud Health Technol Inform. 2001;84:390-3. [PMID: 11604770] MedlineGoogle Scholar6. Wilczynski NL, McKibbon KA, Haynes RB. Search filter precision can be improved by NOTing out irrelevant content. AMIA Annu Symp Proc. 2011;2011:1506-13. [PMID: 22195215] MedlineGoogle Scholar7. Sampson M, Tetzlaff J, Urquhart C. Precision of healthcare systematic review searches in a cross-sectional sample. Res Synth Methods. 2011;2:119-25. [PMID: 26061680] doi:10.1002/jrsm.42 CrossrefMedlineGoogle Scholar8. Hemens BJ, Haynes RB. McMaster Premium LiteratUre Service (PLUS) performed well for identifying new studies for updated Cochrane reviews. J Clin Epidemiol. 2012;65:62-72. [PMID: 21856121] doi:10.1016/j.jclinepi.2011.02.010 CrossrefMedlineGoogle Scholar9. Eden J, Levit L, Berg A, Morton S, eds. Institute of Medicine; Committee on Standards for Systematic Reviews of Comparative Effectiveness Research. Finding What Works in Health Care: Standards for Systematic Reviews. Washington, DC: National Academies Pr; 2011. [PMID: 24983062] doi:10.17226/13059 CrossrefMedlineGoogle Scholar10. Paynter R, Banez LL, Berliner E, Erinoff E, Lege-Matsuura J, Potter S, et al. EPC Methods: An Exploration of the Use of Text-Mining Software in Systematic Reviews. Report no. 16-EHC023-EF. Rockville: Agency for Healthcare Research and Quality; 2016. [PMID: 27195359] Google Scholar Author, Article, and Disclosure InformationAffiliations: From McMaster University, Hamilton, Ontario, Canada.Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M17-1295.Corresponding Author: Alfonso Iorio, MD, PhD, Health Information Research Unit, McMaster University, 1280 Main Street W, CRL-140, Hamilton, Ontario L8S 4K1, Canada; e-mail, [email protected]ca.Current Author Addresses: Mr. Hemens: McMaster University, 1280 Main Street W, HSC 2C, Hamilton, Ontario L3N 8Z5, Canada.Dr. Iorio: Health Information Research Unit, McMaster University, 1280 Main Street W, CRL-140, Hamilton, Ontario M4Y 2X6, Canada.This article was published at Annals.org on 13 June 2017. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoMachine Learning Versus Standard Techniques for Updating Searches for Systematic Reviews: A Diagnostic Accuracy Study Paul G. Shekelle , Kanaka Shetty , Sydne Newberry , Margaret Maglione , and Aneesa Motala Metrics Cited byrevtools: An R package to support article screening for evidence synthesisA question of trust: can we build an evidence base to gain trust in systematic review automation technologies? 1 August 2017Volume 167, Issue 3Page: 210-211KeywordsComputersHealth information technologyInformation technologyMachine learningOsteoporosisPrecision medicineSoftware designSpecificitySystematic reviewsTreatment guidelines ePublished: 13 June 2017 Issue Published: 1 August 2017 Copyright & PermissionsCopyright © 2017 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,285 | 0,745 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,005 |
| Méta-épidémiologie (sens large) | 0,020 | 0,013 |
| Bibliométrie | 0,068 | 0,042 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,025 | 0,018 |
| Science ouverte | 0,010 | 0,010 |
| Intégrité de la recherche | 0,010 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,132 | 0,024 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».