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Maternal and Neonatal Outcomes After Bariatric Surgery: A Systematic Review and Meta-Analysis: Do the Benefits Outweigh the Risks?

2019· review· en· W2912297107 on OpenAlexaff
Wilson Kwong, Gail E. Tomlinson, Denice S. Feig

Bibliographic record

VenueObstetric Anesthesia Digest · 2019
Typereview
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineGestational diabetesObstetricsPreeclampsiaGestational hypertensionOverweightPregnancyObesityMeta-analysisCesarean deliveryGynecologyGestationInternal medicine

Abstract

fetched live from OpenAlex

( Am J Obstet Gynecol . 2018;218(6):573–580) As obesity rates rise, rates of pregnant women who are overweight or obese have increased. Obesity is associated with adverse obstetric and neonatal outcomes, including gestational hypertension, gestational diabetes, preeclampsia, and increased risk of cesarean delivery. Weight loss before pregnancy is recommended to prevent these risks. With an increase in the number of reproductive-age women undergoing bariatric surgery, this study aimed to evaluate the benefits and risks of bariatric surgery on obstetric and neonatal outcomes.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0160.006
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.308
Teacher spread0.229 · 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; both teacher heads agree on what is shown here.

Study designMeta-analysis
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

Citations6
Published2019
Admission routes1
Has abstractyes

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