Successful but limited use of external cephalic version in Auckland
Bibliographic record
Abstract
BACKGROUND: External cephalic version (ECV) can effectively reduce the chance of non-cephalic presentation at birth and reduce caesarean section rate for breech presentation at term. It is recommended in New Zealand to offer ECV to all eligible women with breech presentation at term. AIM: This study aims to determine the ECV success rate at our hospital, factors that predict ECV success, and perinatal outcomes for women who had ECV, and to estimate the ECV attempt rate at our hospital. METHODS: A prospective audit was performed of all women with singleton non-cephalic presentation>or=36 weeks who attended the ECV clinic at National Women's Health in Auckland from July 2002 to January 2006. RESULTS: Two hundred and fifty five women presented for ECV during the study period, and the ECV success rate was 59%. The strongest predictor of ECV success was an unengaged presenting part. Women with successful ECV had a vaginal birth rate of 67%. Three women needed to have an ECV attempt in order to prevent one caesarean section. We estimated that 26% of women with term breech presentation had an ECV attempt. CONCLUSIONS: ECV at National Women's Health is effective at reducing beech presentation at term and at restoring a caesarean section rate equivalent to that of cephalic singleton pregnancy at term. However, the low rate of referral should be addressed.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".