Bibliographic record
Abstract
Resource CornerJanuary 1, 2000Diagnostic Strategies for Common Medical ProblemsSharon E. Straus, MDSharon E. Straus, MDThe Mount Sinai Hospital, Toronto, Ontario, Canada (S.E.S.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJC-2000-132-1-A17 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail At last, the second edition of Diagnostic Strategies for Common Medical Problems has arrived to replace the well-used first edition, published in 1991. Evidence about the precision and accuracy of diagnostic tests is not always readily available, and this resource attempts to overcome the problem. Its aim is to help clinicians with both critical appraisal of diagnostic tests and quantitative decision making about diagnostic strategies. It provides information about the operating characteristics of diagnostic tests and procedures that are commonly used in clinical practice, particularly in internal medicine.Unfortunately, this book does not provide any information on whether the literature was ...Reference1 Wells PS, Anderson DR, Bormanis J, et al. Value of assessment of pretest probability of deep-vein thrombosis in clinical management. Lancet. 1997;350:1795-8. Google Scholar Author, Article, and Disclosure InformationAffiliations: The Mount Sinai Hospital, Toronto, Ontario, Canada (S.E.S.) PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails January 1, 2000Volume 132, Issue 1Page: A17KeywordsConfidence intervalsCreatineDecision makingDeep vein thrombosisDiagnostic medicineElectrocardiographyEnzymesForecastingHematologic testsInfectious diseasesInformation technologyInpatientsIschemiaMyocardial infarctionPercutaneous transluminal coronary angioplastySpecificitySystematic reviewsTroponin ePublished: 9 March 2020 Issue Published: January 1, 2000 Copyright & PermissionsCopyright © 2000 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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.138 | 0.059 |
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".