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Record W2101125163 · doi:10.1016/j.ijgo.2010.12.003

Oral contraceptives and the risk of venous thromboembolism: An update

2011· article· en· W2101125163 on OpenAlexaffabout
Robert L. Reid, Nicholas Leyland, Wendy Wolfman, Catherine Allaire, Alaa Awadalla, Carolyn Best, Sheila Dunn, Madeleine Lemyre, Violaine Marcoux, Chantal Ménard, Frank Potestio, David Rittenberg, Sukhbir S. Singh, Vyta Senikas

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2011
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsCongress of Aboriginal PeoplesManitoba Beekeepers' AssociationThunder Bay Regional Research InstituteOntario Neurotrauma FoundationNorth York General Hospital
Fundersnot available
KeywordsMedicineObservational studyGuidelineMEDLINEVenous thromboembolismRandomized controlled trialSpecialtyFamily medicineAlternative medicineEvidence-based medicineSystematic reviewIntensive care medicineFamily planningClinical trialHormonal contraceptionGynecologyPopulationSurgeryEnvironmental healthPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide current and emerging evidence on oral contraceptives and the risk of venous thromboembolism. EVIDENCE: Articles published in English from 2005 were retrieved through searches of PubMed and Medline, using the following terms: venous thromboembolism, VTE, contraception, oral contraceptives, hormonal contraception. Results were restricted to systematic reviews, randomized control trials/controlled clinical trials, and observational studies. Searches were updated on a regular basis and incorporated in the guideline to May 2010. Grey (unpublished) literature was identified through searching the websites of health technology assessment and health technology assessment-related agencies, clinical practice guideline collections, clinical trial registries, and national and international medical specialty societies. VALUES: The quality of evidence was rated using the criteria described by the Canadian Task Force on Preventive Health Care (Table).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.302
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2011
Admission routes2
Has abstractyes

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