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Record W2111096898 · doi:10.3122/jabfm.18.1.8

Episiotomy in Low-Risk Vaginal Deliveries

2005· article· en· W2111096898 on OpenAlexaff
Roland E. Allen, Russell W. Hanson

Bibliographic record

VenueThe Journal of the American Board of Family Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsChinook Regional HospitalAlberta Energy
Fundersnot available
KeywordsEpisiotomyMedicineOdds ratioObstetricsConfidence intervalIncidence (geometry)ForcepsRetrospective cohort studyVaginal deliveryRisk factorPregnancyGynecologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The object of this study was to determine factors leading to episiotomy in low-risk vaginal deliveries, including a comparison of family physicians with obstetricians. The research was also to assess the incidence of episiotomy in a large community hospital and compare it with a national rate of 40%. METHODS: A retrospective cohort design was used with computerized records from one hospital. Demographic and clinical information was extracted from the database, including parity, age, physician type, anesthesia, induction, fetal complications, and other factors. Only low-risk vaginal deliveries (n = 3120) from the year 2003 were included. RESULTS: There was an overall episiotomy incidence of 48%; obstetricians performed episiotomy in 54% of their low-risk patients and family physicians in 33% of similar women (P < .001). Adjusted for multiple factors, the odds ratio for obstetricians performing episiotomy was 2.38 [1.98 to 2.87 (95% confidence interval (CI))]. Instrument-assisted delivery was the strongest predictor for episiotomy, with an adjusted odds ratio for forceps of 5.08 [3.75 to 6.88 CI], and vacuum 2.86 [1.78 to 4.58 CI]. CONCLUSION: Episiotomy in this hospital is being performed in almost half of all vaginal births. Obstetricians are more than twice as likely to perform episiotomy as family physicians in similar patients. Instrument-assisted delivery is a strong risk factor for episiotomy.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.016
GPT teacher head0.296
Teacher spread0.279 · 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 designObservational
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

Citations18
Published2005
Admission routes1
Has abstractyes

Explore more

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