Bibliographic record
Abstract
Resource CornerJanuary 1, 2002Evidence-based HypertensionDaniel M. Panisko, MD, MPHDaniel M. Panisko, MD, MPHUniversity Health Network, University of Toronto, Toronto, Ontario, Canada (D.M.P.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJC-2002-136-1-A14 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail Evidence-Based Hypertension has successfully achieved its goal to be a “practice-based textbook” that provides primary care practitioners with an evidence-based approach to the management of hypertension. The authors have also succeeded admirably in achieving their secondary goal of providing patient-centered approaches.The almost pocket-sized handbook is clearly organized with chapters whose titles frame the vital questions in hypertension management. An opening chapter orients readers to the book’s purpose, philosophy, and style. Diagnosis of hypertension, impact of cardiovascular risk factors, treatment of hypertension, integration of management with comorbid conditions, continuing care, difficult clinical situations, and hypertension in pregnancy are all comprehensively discussed.Each ... Author, Article, and Disclosure InformationAffiliations: University Health Network, University of Toronto, Toronto, Ontario, Canada (D.M.P.) PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails January 1, 2002Volume 136, Issue 1Page: A14 ePublished: 9 March 2020 Issue Published: January 1, 2002 Copyright & PermissionsCopyright © 2002 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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.444 | 0.202 |
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".