Preconception-initiated, low-dose aspirin did not increase live birth after 1 or 2 pregnancy losses
Bibliographic record
Abstract
ACP Journal Club16 September 2014Preconception-initiated, low-dose aspirin did not increase live birth after 1 or 2 pregnancy lossesJoel G. Ray, MD, MScJoel G. Ray, MD, MScSt. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada (J.G.R.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-161-6-201409160-02013 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Source CitationSchisterman EF, Silver RM, Lesher LL, et al. Preconception low-dose aspirin and pregnancy outcomes: results from the EAGeR randomised trial. Lancet. 2014;384:29-36. https://pubmed.ncbi.nlm.nih.gov/24702835Clinical Impact RatingsGIM/FP/GP: Reference1 Kaandorp SP, Goddijn M, van der Post JA, et al. Aspirin plus heparin or aspirin alone in women with recurrent miscarriage. N Engl J Med. 2010;362:1586-96. [PMID: 20335572] Google Scholar Author, Article, and Disclosure InformationAuthors: Joel G. Ray, MD, MScAffiliations: St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada (J.G.R.)This article was published at Annals.org on 2 September 2014. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics 16 September 2014Volume 161, Issue 6Page: JC13KeywordsAspirinBirthBirth ratesChild healthHuman chorionic gonadotropinMenstrual cyclePregnancyRelative riskResearch fundingTermination of pregnancy ePublished: 16 September 2014 Issue Published: 16 September 2014 Copyright & PermissionsCopyright © 2014 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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.005 |
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".