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Record W4388745658 · doi:10.1055/s-0043-1776795

Navigating the Role of Anti-Obesity Agents Prior to Pregnancy: A Narrative Review

2023· review· en· W4388745658 on OpenAlexaff
Alyse S. Goldberg, Samin Dolatabadi, Heidi Dutton, Jamie L. Benham

Bibliographic record

VenueSeminars in Reproductive Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsUniversity of OttawaUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsPregnancyObesityNarrative reviewMedicineWeight lossFertilityIntervention (counseling)ObstetricsGynecologyIntensive care medicineEndocrinologyEnvironmental healthPopulationPsychiatryBiology

Abstract

fetched live from OpenAlex

Utilization of anti-obesity agents is rising in reproductive-age females with some planning for future pregnancy. Lifestyle-induced weight loss has been shown to increase spontaneous conception rate, improve rates of fertility intervention complications, and decrease pregnancy comorbidities. However, the definitive role of assisting weight loss with medication prior to pregnancy remains to be established. The implications of anti-obesity agent used prior to pregnancy are explored in this narrative review, considering benefits of weight loss as well as available evidence for use and risks of anti-obesity agents prior to pregnancy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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 science0.0010.001
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 head0.070
GPT teacher head0.443
Teacher spread0.373 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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