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Record W4308834461 · doi:10.1055/s-0042-1758062

The ART of Thromboprophylaxis in the Prevention of Gestational Venous Thromboembolism

2022· review· en· W4308834461 on OpenAlexaff
Elvira Grandone, Doris Barcellona, Mariano Intrieri, Giovanni Luca Tiscia, Luigi Nappi, Maha Othman

Bibliographic record

VenueSeminars in Thrombosis and Hemostasis · 2022
Typereview
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsSt. Lawrence CollegeQueen's University
Fundersnot available
KeywordsOvarian hyperstimulation syndromeMedicinePregnancyThrombophiliaGynecologyOvulation inductionMiscarriagePolycystic ovaryAssisted reproductive technologyBody mass indexObstetricsThrombosisInfertilityOvulationSurgeryIn vitro fertilisationInternal medicineHormoneObesity

Abstract

fetched live from OpenAlex

Assisted reproductive techniques (ART) allow infertile couples to conceive. Use of hormones to obtain a controlled ovarian stimulation and an adequate growth of the endometrium preparatory for embryo implantation are not riskless. Among others, thrombotic events can occur during the ovulation induction or pregnancy following ART. As the number of women approaching ART to conceive is steadily increasing, the issue of thrombotic risk in this setting is relevant. Data on the weight of each risk factor and on potential benefit of thromboprophylaxis are largely lacking. In this review, we discuss risk of venous thromboembolism during pregnancy following ART, with a focus on general (i.e.: age, body mass index, thrombophilia, bed rest, transfusions) and ART-specific (i.e., polycystic ovarian syndrome, ovarian hyperstimulation syndrome) risk factors. We also attempt to provide some suggestions to guide clinical practice, based on available data and studies performed outside ART.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.091
GPT teacher head0.373
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2022
Admission routes1
Has abstractyes

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