Assessment of Recurrence Risk After Unprovoked Venous Thromboembolism
Bibliographic record
Abstract
Letters3 May 2011Assessment of Recurrence Risk After Unprovoked Venous ThromboembolismMarc A. Rodger, MD, MSc, Timothy Ramsay, PhD, Gregoire Le Gal, MD, PhD, and Marc Carrier, MD, MScMarc A. Rodger, MD, MScFrom Ottawa Hospital, Ottawa, Ontario K1H 8L6, Canada.Search for more papers by this author, Timothy Ramsay, PhDFrom Ottawa Hospital, Ottawa, Ontario K1H 8L6, Canada.Search for more papers by this author, Gregoire Le Gal, MD, PhDFrom Ottawa Hospital, Ottawa, Ontario K1H 8L6, Canada.Search for more papers by this author, and Marc Carrier, MD, MScFrom Ottawa Hospital, Ottawa, Ontario K1H 8L6, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-154-9-201105030-00015 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:We congratulate Douketis and colleagues on an excellent collaborative project that yields useful insights into defining who is at high risk for recurrent venous thromboembolism (VTE) (1). Confounding among age, estrogen-associated VTE, and sex poses an important challenge in the analysis of the authors' data because of the weak association between VTE and estrogen exposure due to pregnancy, oral contraceptives, and hormone replacement therapy (2); the fact that estrogen exposure does not occur in men and rarely occurs in older women; and the apparent association between older age and recurrent VTE in women (3).A robust analysis ...References1. Douketis J, Tosetto A, Marcucci M, Baglin T, Cushman M, Eichinger S, et al. Patient-level meta-analysis: effect of measurement timing, threshold, and patient age on ability of d-dimer testing to assess recurrence risk after unprovoked venous thromboembolism. Ann Intern Med. 2010;153:523-31. [PMID: 20956709] LinkGoogle Scholar2. Renoux C, Dell'Aniello S, Suissa S. Hormone replacement therapy and the risk of venous thromboembolism: a population-based study. J Thromb Haemost. 2010;8:979-86. [PMID: 20230416] MedlineGoogle Scholar3. Rodger MA, Kahn SR, Wells PS, Anderson DA, Chagnon I, Le Gal G, et al. Identifying unprovoked thromboembolism patients at low risk for recurrence who can discontinue anticoagulant therapy. CMAJ. 2008;179:417-26. [PMID: 18725614] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Marc A. Rodger, MD, MSc; Timothy Ramsay, PhD; Gregoire Le Gal, MD, PhD; Marc Carrier, MD, MScAffiliations: From Ottawa Hospital, Ottawa, Ontario K1H 8L6, Canada.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoPatient-Level Meta-analysis: Effect of Measurement Timing, Threshold, and Patient Age on Ability of d-Dimer Testing to Assess Recurrence Risk After Unprovoked Venous Thromboembolism James Douketis , Alberto Tosetto , Maura Marcucci , Trevor Baglin , Mary Cushman , Sabine Eichinger , Gualtiero Palareti , Daniela Poli , R. Campbell Tait , and Alfonso Iorio Assessment of Recurrence Risk After Unprovoked Venous Thromboembolism Alfonso Iorio and James Douketis Metrics 3 May 2011Volume 154, Issue 9Page: 644KeywordsBody mass indexConflicts of interestEstrogensFactor analysisHormone replacement therapyMedical risk factorsOral contraceptive therapyPregnancyVenous thromboembolism ePublished: 3 May 2011 Issue Published: 3 May 2011 Copyright & PermissionsCopyright © 2011 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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".