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P4433Long-term risk of cardiovascular disease following fertility therapy: systematic review and meta-analysis

2017· article· en· W2762209019 on OpenAlexaff
Natalie Dayan, Kristian B. Filion, Marisa Okano, Caitlin Kilmartin, S.L. Reinblatt, Tara Landry, Olga Basso, Jacob A. Udell

Bibliographic record

VenueEuropean Heart Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsWomen's College HospitalJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsMedicineMeta-analysisTerm (time)DiseaseIntensive care medicineFertilityInternal medicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Importance: The use of fertility therapy is rising, but its long-term cardiovascular impact is uncertain. Objective: To summarize evidence linking the use of fertility therapy to long-term cardiovascular outcomes. Methods: The following databases were searched without language limits from 1946–2016: MEDLINE; Embase Classic + Embase; BIOSIS Previews; POPLINE; CINAHLPlus; The Cochrane Central Register of Controlled Trials; The Database of Abstracts of Reviews of Effects; The Cochrane Database of Systematic Reviews; LILACS; Web of Science; Scopus; manual references; and Clinical Trials registries. Two independent reviewers screened studies and assessed study quality. Inclusion criteria were human study; case control, cohort, or randomized trial design; exposure to fertility therapy clearly reported; any cardiovascular outcome; control group without fertility therapy; minimum follow-up of 1 year; estimates adjusted for age. We used random effects models to pool hazard ratio (HR) with 95% confidence interval (CI) across studies of outcomes (cardiac event, stroke, venous thromboembolism, hypertension, and/or diabetes mellitus) comparing women who received fertility therapy with those who did not.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.097
GPT teacher head0.335
Teacher spread0.238 · 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 designMeta-analysis
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

Citations1
Published2017
Admission routes1
Has abstractyes

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