The anesthetic approach to repeated cesarean sections: A prospective cohort study
Bibliographic record
Abstract
Objective: Each repeat cesarean section (CS) potentially adds surgical complexity. The determination of appropriate anesthesia strategy to meet the surgical challenge is of crucial importance for the maternal and neonatal outcome. Study design: This prospective cohort study was conducted from 1-Jan-2021 to 31-Dec-2021 at a single large obstetric centre of all repeat CS. We compared the characteristics and the appropriateness of the anesthesia techniques for low-order repeat CS (LOR-CS) (1 or 2 previous CS) and high order repat CS (HOR-CS) group (3 or more repeat CS). Results: During the study period, 1057 parturients met the study entry criteria, with 821 parturients in the LOR-CS group and 236 parturients in the HOR-CS group. The use of spinal anesthesia was more common for HOR-CS 84.3%. Overall surgical time varied between LOR-CS (38 min, 29-49) and HOR-CS (42 min, 31-57) (p = 0.004).The rate of moderate and severe adhesions was relatively high in HOR-CS and the duration of overall surgical time for cases with mild adhesions was 38 min (29-48), for moderate adhesions was 44 min (34.8-56.5), and for severe adhesions was 56 min (44.8-74.3). There was no significant difference in the Estimated Blood Loss (EBL) between LOR-CS and HOR-CS, with values of 653 ± 292 ml vs. 660 ± 285 ml, respectively. Conclusion: Our data indicate that spinal anesthesia, standard monitoring and regular anesthetic setup are safe and suitable for the majority of HOR-CS, except in cases with high suspicion of placental accreta spectrum.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".