Of studies, syntheses, synopses, and systems: the “4S” evolution of services for finding current best evidence
Bibliographic record
Abstract
EditorialMarch 1, 2001Of studies, syntheses, synopses, and systems: the "4S" evolution of services for finding current best evidenceR. Brian Haynes, MD, PhDR. Brian Haynes, MD, PhDMcMaster University, Hamilton, Ontario, Canada (R.B.H.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJC-2001-134-2-A11 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail Practical resources to support evidence-based health care decisions are rapidly evolving. New and better services are being created through the combined forces of increasing numbers of clinically important studies, increasingly robust evidence synthesis and synopsis services, and better information technology and systems. The need for these resources is spurred by demands for higher quality at lower cost from health services, but the impact of better information resources is being blunted by noisy pretenders promising "the earth" but yielding just the dirt. Providers and consumers of evidence-based health care can help themselves to best current evidence by recognizing and using the ...References1 Haynes RB, Sackett DL, Gray JR, Cook DL, Guyatt GH. Transferring evidence from research into practice: 1. The role of clinical care research evidence in clinical decisions [Editorial]. ACP J Club. 1996 Nov-Dec;125:A14-6. Google Scholar2 Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA. 1998;280:1339-46. Google Scholar Author, Article, and Disclosure InformationAffiliations: McMaster University, Hamilton, Ontario, Canada (R.B.H.) PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Cited byWelcome, PIER, A New Physicians' Information and Education ResourceRobert G. Badgett, MD and Cynthia D. Mulrow, MD, MSc March 1, 2001Volume 134, Issue 2Page: A11KeywordsAntibioticsCommon coldComputersComputing systemsDatabasesElectronic medical recordsEvidence based medicineFallsHealth careHealth care providersInformation technologyInternetLibrariesMedical servicesPenicillinRespiratory systemSafetySystematic reviewsTetracyclinesUpper respiratory tract infections ePublished: 9 March 2020 Issue Published: March 1, 2001 Copyright & PermissionsCopyright © 2001 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.080 | 0.387 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".