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Record W2026978238 · doi:10.1055/s-2007-976547

Adherence to Guidelines on the Management of Dystocia and Cesarean Section Rates

2007· article· en· W2026978238 on OpenAlexaffabout
Lawrence Oppenheimer, Paul Holmes, Qiuying Yang, Tubao Yang, Mark Walker, Shi Wu Wen

Bibliographic record

VenueAmerican Journal of Perinatology · 2007
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineGuidelineObstetricsGestationPregnancyGynecologySingletonIncidence (geometry)

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate to what extent the Society of Obstetricians and Gynecologists of Canada (SOGC) guidelines on dystocia are being followed, and whether adherence to the guidelines is related to cesarean section rates. Data were extracted from a maternity database for nulliparous women with singleton, cephalic pregnancies at 37 or more completed weeks of gestation for a 4-year period. Patients delivered by elective cesarean section were excluded. Data were examined to determine whether those who had a cesarean section for dystocia in the first stage of labor fulfilled SOGC guidelines. In addition, the obstetricians were divided into two groups (high or low) according to their cesarean section rate for dystocia to determine whether a higher section rate was associated with an increased guideline violation rate. There were 239 nulliparous women who had a cesarean section for dystocia in the first stage of labor. The guidelines were followed in 47.7% of spontaneous labors and 77.5% of inductions. The mean section rate for dystocia in the first stage of labor was 10.8% in the high group and 6.6% in the low group, and the incidence of guideline violations in these groups was 48.0% and 39.6%, respectively ( P = 0.07). The study had a power of 0.88 to detect a 40% difference in guideline violation rates between the two groups. We conclude that many women have cesarean section for dystocia performed without fulfilling SOGC guidelines.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.158

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.057
GPT teacher head0.416
Teacher spread0.359 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations11
Published2007
Admission routes2
Has abstractyes

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