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Sonographic Lower Uterine Segment Thickness After Prior Cesarean Section to Predict Uterine Rupture: A Systematic Review and Meta-analysis

2020· review· en· W3030157609 on OpenAlexaff
Brenna Swift, Prakesh S. Shah, D. Farine

Bibliographic record

VenueObstetric Anesthesia Digest · 2020
Typereview
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineUterine ruptureMeta-analysisUltrasoundObstetricsPredictive valueGynecologyUterusRadiologyInternal medicine

Abstract

fetched live from OpenAlex

(Anaesthesia. 2019;74:850–855) The rate of trial of labor after cesarean (TOLAC) in the United States has markedly dropped over the past few decades. Uterine rupture is the most serious complication associated with TOLAC, with the risk ranging from 0.52% for women in spontaneous labor to 2.45% for women undergoing labor induction with prostaglandin. This risk may be contributing to the declining rate of TOLAC. Sonographic measurement of the lower uterine segment thickness has been evaluated by several prospective studies, but the cutoff values for recommending TOLAC in these studies have differed due to varying ultrasound methodologies. This study analyzed the ultrasound methodology used in each study and then conducted a meta-analysis of the predictive value of sonographic measurement of the lower uterine segment thickness for uterine rupture during labor.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.021
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.049
GPT teacher head0.344
Teacher spread0.294 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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