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Record W4315928684 · doi:10.1002/ijgo.14538

The challenges of obesity for fertility: A <scp>FIGO</scp> literature review

2023· review· en· W4315928684 on OpenAlexaff
Divya Gautam, Nikhil Purandare, Cynthia Maxwell, Mary Rosser, Patrick O’Brien, Edgar Mocanu, Ciaran Mckeown, Jaideep Malhotra, Fionnuala M. McAuliffe

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2023
Typereview
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineInfertilityFertilityPsychosocialObesityScope (computer science)Multidisciplinary approachEmpathyGerontologyPregnancyGynecologyEnvironmental healthPopulationEndocrinologyPsychiatry

Abstract

fetched live from OpenAlex

Obesity has been linked to infertility through several mechanisms, including at a molecular level. Those living with obesity face additional barriers to accessing fertility treatments and achieving a successful pregnancy, which can contribute to their economic and psychosocial stressors. There is scope to further improve care for people living with obesity and infertility with empathy, via a multidisciplinary approach.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.174
GPT teacher head0.502
Teacher spread0.329 · 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 designSystematic review
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

Citations57
Published2023
Admission routes1
Has abstractyes

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