<scp>d</scp>-Dimer and Venous Thromboembolism
Bibliographic record
Abstract
Letters21 September 2004d-Dimer and Venous ThromboembolismRussell D. Hull, MBBS, MSc, William A. Ghali, MD, Rollin F. Brant, PhD, MPH, and Paul D. Stein, MDRussell D. Hull, MBBS, MScFrom University of Calgary, Calgary, Alberta T2N 2T9, Canada, and Saint Joseph Mercy Oakland, Pontiac, MI 48341.Search for more papers by this author, William A. Ghali, MDFrom University of Calgary, Calgary, Alberta T2N 2T9, Canada, and Saint Joseph Mercy Oakland, Pontiac, MI 48341.Search for more papers by this author, Rollin F. Brant, PhD, MPHFrom University of Calgary, Calgary, Alberta T2N 2T9, Canada, and Saint Joseph Mercy Oakland, Pontiac, MI 48341.Search for more papers by this author, and Paul D. Stein, MDFrom University of Calgary, Calgary, Alberta T2N 2T9, Canada, and Saint Joseph Mercy Oakland, Pontiac, MI 48341.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-141-6-200409210-00022 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:Dr. Wolf has identified the upper 95% confidence limit for the quantitative rapid ELISA's negative likelihood ratio from the sensitivity analysis. The lower 95% confidence limit was 0.00, which is statistically as likely as the value for the upper 95% limit. Both of these extreme values are unlikely to occur clinically. The sensitivity analysis provided a central estimate of 0.05, which is consistent with the primary analyses. It should be noted that the value for sensitivity in the sensitivity analysis was 0.98, with a 95% confidence limit of 0.88 to 1.00, a much narrower range of values than ...Reference1. Perrier A, Roy PM, Aujesky D, Chagnon I, Howarth N, Gourdier AL, et al . Diagnosing pulmonary embolism in outpatients with clinical assessment, D-dimer measurement, venous ultrasound, and helical computed tomography: a multicenter management study. Am J Med. 2004;116:291-9. [PMID: 14984813] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From University of Calgary, Calgary, Alberta T2N 2T9, Canada, and Saint Joseph Mercy Oakland, Pontiac, MI 48341. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee Alsod-Dimer for the Exclusion of Acute Venous Thrombosis and Pulmonary Embolism Paul D. Stein , Russell D. Hull , Kalpesh C. Patel , Ronald E. Olson , William A. Ghali , Rollin Brant , Rita K. Biel , Vinay Bharadia , and Neeraj K. Kalra d-Dimer and Venous Thromboembolism Stephen J. Wolf d-Dimer and Venous Thromboembolism Grégoire Le Gal , Marc Righini , and Henri Bounameaux d-Dimer and Venous Thromboembolism John T. Philbrick , Steven Heim , and Joel M. Schectman d-Dimer and Venous Thromboembolism Fabio Puglisi and Edda Federico Metrics 21 September 2004Volume 141, Issue 6Page: 483KeywordsConfidence limitD-dimerEnzyme linked immunosorbent assayLikelihood ratioOutpatientsPulmonary embolismQuantitative analysisSpecificityUltrasound imagingVenous thromboembolism ePublished: 21 September 2004 Issue Published: 21 September 2004 CopyrightCopyright © 2004 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.182 | 0.028 |
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".