Of studies, syntheses, synopses, summaries, and systems: the “5S” evolution of information services for evidence-based health care decisions
Bibliographic record
Abstract
EditorialNovember 1, 2006Of studies, syntheses, synopses, summaries, and systems: the "5S" evolution of information services for evidence-based health care decisionsR. 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-2006-145-3-A08 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail Success in delivering evidence-based health care relies heavily on the ready availability of current best evidence about diagnosis, treatment, and prevention options for health disorders, ideally tailored to the characteristics and context of the individual patient or population and the resources of the provider. While existing information resources fall short of perfection, the past decade has seen considerable progress, and an attractive array of services is now available for many health care decisions. Providers and consumers of evidence-based health care can help themselves to the best current evidence by recognizing the most "evolved" information services in the topic areas of ...References1 Haynes RB. Of studies, syntheses, synopses, and systems: the "4S" evolution of services for finding current best evidence. ACP J Club. 2001 Mar-Apr;134(2):A11-3. [PMID: 11280129] Google Scholar2 Box GE. Robustness in the strategy of scientific model building. In: Launer RL, Wilkinson GN, eds. Robustness in Statistics. New York: Academic Press;1979. Google Scholar3 Mallett S, Clarke M. How many Cochrane reviews are needed to cover existing evidence on the effects of health care interventions? ACP J Club. 2003 Jul-Aug;139(1):A11. [PMID: 12841730] Google Scholar Author, Article, and Disclosure InformationAffiliations: McMaster University, Hamilton, Ontario, Canada (R.B.H.) PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails November 1, 2006Volume 145, Issue 3Page: A8 ePublished: 9 March 2020 Issue Published: November 1, 2006 Copyright & PermissionsCopyright © 2006 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 | 0.169 | 0.563 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.028 | 0.015 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.017 | 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".