Bibliographic record
Abstract
Letters4 July 2006Clinical Sensibility and Barriers to Knowledge TranslationJamie C. Brehaut, PhD and Ian G. Stiell, MD, MScJamie C. Brehaut, PhDFrom Ottawa Health Research Institute, University of Ottawa, Ottawa K1Y 4E9, Ontario, Canada.Search for more papers by this author and Ian G. Stiell, MD, MScFrom Ottawa Health Research Institute, University of Ottawa, Ottawa K1Y 4E9, Ontario, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-145-1-200607040-00017 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Reilly and Evans (1) have provided a very useful discussion of how to study the impact of clinical prediction and decision rules in actual practice. Most rules of this type have not received such evaluations; it is hoped that this work will encourage researchers to begin to fill in this gap. We have 3 specific comments about the article.First, the authors' introduction states, “How frequently these and other prediction rules are being used in clinical practice is not known.” This is certainly true of the vast majority of rules, but a fair bit is now known ...References1. Reilly BM, Evans AT. Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med. 2006;144:201-9. [PMID: 16461965] LinkGoogle Scholar2. Graham ID, Stiell IG, Laupacis A, O'Connor AM, Wells GA. Emergency physicians' attitudes toward and use of clinical decision rules for radiography. Acad Emerg Med. 1998;5:134-40. [PMID: 9492134] CrossrefMedlineGoogle Scholar3. Graham ID, Stiell IG, Laupacis A, McAuley L, Howell M, Clancy M, et al. Awareness and use of the Ottawa ankle and knee rules in 5 countries: can publication alone be enough to change practice? Ann Emerg Med. 2001;37:259-66. [PMID: 11223761] CrossrefMedlineGoogle Scholar4. Brehaut JC, Stiell IG, Visentin L, Graham ID. Clinical decision rules “in the real world”: how a widely disseminated rule is used in everyday practice. Acad Emerg Med. 2005;12:948-56. [PMID: 16166599] CrossrefMedlineGoogle Scholar5. Brehaut JC, Stiell IG, Graham ID. Will a new clinical decision rule be widely used? The case of the Canadian C-spine rule. Acad Emerg Med. 2006;13:413-20. [PMID: 16531607] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Jamie C. Brehaut, PhD; Ian G. Stiell, MD, MScAffiliations: From Ottawa Health Research Institute, University of Ottawa, Ottawa K1Y 4E9, Ontario, Canada.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoTranslating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions Brendan M. Reilly and Arthur T. Evans Metrics Cited byMeasuring Acceptability of Clinical Decision Rules: Validation of the Ottawa Acceptability of Decision Rules Instrument (OADRI) in Four CountriesDevelopment of the Capacity Necessary to Perform and Promote Knowledge Translation Research in Emergency Medicine 4 July 2006Volume 145, Issue 1Page: 77-78KeywordsAnklesConflicts of interestFactor analysisKneesSafetySpecificity ePublished: 4 July 2006 Issue Published: 4 July 2006 Copyright & PermissionsCopyright © 2006 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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 | 0.368 | 0.802 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.065 | 0.020 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".