Finding the gold in MEDLINE: Clinical Queries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 ...
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it