PLD.54 The influence of Operator characteristics in the rate of Caesarean Section in the Second Stage of Labour
Bibliographic record
Abstract
Introduction The aim of this study was to identify operator characteristics that may influence the rate of CS in the second stage compared to overall rate of CS. Methods Within this tertiary level unit in Canada, the labour ward is staffed full time by two trained obstetric staff, supported by trainees; the ultimate decisions regarding mode of delivery is made between staff and patient. Caesarean sections performed in the second stage of labour were identified from medical records and classified by the staff making the decision to perform the operation. Results Between January 2007 and December 2012, 29,779 infants were delivered in Mount Sinai Hospital. Of these, 11,140 infants were delivered by caesarean section, of which 821 were CS in the second stage of labour (7.3% of all CS, 2.7% of all deliveries overall). Male obstetricians had a lower rate of CS in the second stage compared to female obstetricians (male 7.2% vs. female 9% p = 0.01). Staff practicing less than ten years since graduation had a higher rate of CS in the second stage than those with greater than ten years post graduation experience (11% (<10 years) vs. 7% (>10 years) p < 0.01). Conclusion While fetal and maternal factors can influence the decision on mode of delivery in the second stage of labour, the personal characteristics of the operator may also have an effect. Operators may benefit from this knowledge in awareness of how this may affect their counselling of women.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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.
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".