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Record W2024917977 · doi:10.1016/s0029-7844(99)00609-2

Much ado about a little cut: is episiotomy worthwhile?

2000· article· en· W2024917977 on OpenAlexaff
Erica Eason

Bibliographic record

VenueObstetrics and Gynecology · 2000
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEpisiotomyMedicinePerineumChildbirthForcepsIntervention (counseling)Psychological interventionPelvic floorForceps deliveryObstetricsMalpracticePregnancyVaginal deliveryNursingSurgery

Abstract

fetched live from OpenAlex

Methods to prevent perineal trauma during childbirth include avoiding episiotomy and forceps delivery and slowing delivery of the head to allow the perineum time to stretch. Each intervention can lengthen the second stage of labor and change the biophysical stresses on infants and the pelvic floor. Available evidence supports the belief that the interventions are safe for infants and do not lead to significant short- or long-term maternal morbidity. We should abandon the conventional teaching that a longer second stage and perineal stretching are harmful. Routine episiotomy is no longer advisable. Forces that might inhibit physicians from practicing evidence-based techniques of obstetric delivery include time pressures, malpractice concerns, lack of experience with slow perineal stretching, and an interventionist practice pattern. Changes in practice can be effectively introduced through consumer pressures, opinion leaders, and in teaching institutions, by house staff.

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)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0030.007
Open science0.0010.001
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0120.002

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 head0.011
GPT teacher head0.256
Teacher spread0.245 · 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 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

Citations40
Published2000
Admission routes1
Has abstractyes

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