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Record W4403045972 · doi:10.1016/j.rbmo.2024.104456

The investigation and management of recurrent early pregnancy loss: a Canadian Fertility and Andrology Society clinical practice guideline

2024· article· en· W4403045972 on OpenAlexaffabout
Sony Sierra, Jason Min, Julio Saumet, Heather Shapiro, Camille Sylvestre, Jeff Roberts, Kimberly Liu, William Buckett, Maria P. Vélez, Neal Mahutte

Bibliographic record

VenueReproductive BioMedicine Online · 2024
Typearticle
Languageen
FieldMedicine
TopicEctopic Pregnancy Diagnosis and Management
Canadian institutionsOttawa Fertility CentreMcGill UniversityQueen's UniversityPacific Centre for Reproductive MedicineWomen's College HospitalCentre Hospitalier Universitaire Sainte-JustineSinai Health SystemUniversité de MontréalUniversity of Alberta
Fundersnot available
KeywordsGuidelineFertilityGynecologyMedicineClinical PracticeAndrologyPregnancyObstetricsFamily medicineBiologyPopulationEnvironmental healthPathology

Abstract

fetched live from OpenAlex

This guideline defines recurrent early pregnancy loss (REPL) as two or more losses that occur before 10 weeks gestational age and includes non-consecutive and biochemical losses. Investigations should be considered on an individual basis and may include an evaluation of genetic, anatomical, endocrinological, structural and male-associated factors. Based on the findings and available resources, options for management may include preimplantation genetic testing (PGT) for aneuploidies or PGT for chromosomal structural rearrangements, progesterone supplementation and supportive care. This guideline emphasizes a personalized approach to the problem of REPL, recognizing an overall promising prognosis for this patient population and the avoidance of treatment options that have not been shown to be of benefit.

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)0.006
metaresearch head score (Gemma)0.018
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.567
Threshold uncertainty score0.870

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.005
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0060.003
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0040.002

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 head0.042
GPT teacher head0.378
Teacher spread0.336 · 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
GenreMethods

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

Citations6
Published2024
Admission routes2
Has abstractno

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