Finding the gold in MEDLINE: Clinical Queries
Bibliographic record
Abstract
EditorialJanuary 1, 2005Finding the gold in MEDLINE: Clinical QueriesR. Brian Haynes, MD, PhD, Nancy Wilczynski, MScR. Brian Haynes, MD, PhDHealth Information Research Unit, McMaster University, Hamilton, Ontario, Canada (R.B.H., N.W.), Nancy Wilczynski, MScHealth Information Research Unit, McMaster University, Hamilton, Ontario, Canada (R.B.H., N.W.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJC-2005-142-1-A08 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail MEDLINE is the premier source for access to the broad spectrum of medical literature. With > 15 000 000 references from > 4600 biomedical journals, the MEDLINE treasure trove contains citations for virtually all the gold that biomedical research enterprise has to offer.But finding exactly what you want in such a huge database has its challenges. First, the indexing is fairly coarsely grained, so it can be difficult to specify exactly what you are seeking. Second, the English language is notorious for synonyms, homonyms, eponyms, and neologisms, making it impossible to include all the possible variants, while at the same time ensuring that ... Author, Article, and Disclosure InformationAffiliations: Health Information Research Unit, McMaster University, Hamilton, Ontario, Canada (R.B.H., N.W.) PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails January 1, 2005Volume 142, Issue 1Page: A8KeywordsAttentionDatabasesEtiologyEvidence based medicineHealth careHealth services researchLibrariesQualitative studiesSpecificityTreatment guidelines ePublished: 9 March 2020 Issue Published: January 1, 2005 Copyright & PermissionsCopyright © 2005 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 | 0.010 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.034 |
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".