Bibliographic record
Abstract
Resource CornerNovember 1, 2004Ebm2goSharon E. Straus, MD, MSc, FRCPCSharon E. Straus, MD, MSc, FRCPCUniversity of Toronto, Toronto, Ontario, Canada (S.E.S.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJC-2004-141-3-A13 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail We all struggle to keep abreast of the clinical literature, and many resources have been developed for personal digital assistants (PDAs) to provide evidence at the point of care. Ebm2go (www.ebm2go.com) is a resource developed for use on Palms and Pocket PCs to help clinicians meet this challenge. Although its target audience is not explicitly stated on the Web site, it appears to be aimed at family physicians and general internists. I downloaded this free software to a Pocket PC and explored its usefulness while attending on a general medicine clinical teaching unit.Ebm2go is divided into 4 sections: Formulary, Guidelines, ... Author, Article, and Disclosure InformationAffiliations: University of Toronto, Toronto, Ontario, Canada (S.E.S.) Previousarticle Advertisement FiguresReferencesRelatedDetails November 1, 2004Volume 141, Issue 3Page: A13KeywordsAdverse eventsCardiovascular disease riskCholesterolDatabasesDementiaDrug informationDrugsHypertensionProspective studiesSARS coronavirus ePublished: 9 March 2020 Issue Published: November 1, 2004 Copyright & PermissionsCopyright © 2004 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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.886 | 0.752 |
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".