Quality of recovery after cesarean delivery in patients with Class III obesity: a prospective observational cohort study
Bibliographic record
Abstract
BACKGROUND: With obesity, the post-operative period is characterized by an increased and prolonged inflammatory response. This study evaluated the impact of obesity on postpartum recovery after elective cesarean delivery, using the Obstetric Quality of Recovery Score-10 (ObsQoR-10). METHODS: A prospective observational cohort study was conducted with 127 patients divided into two groups: Control (BMI <30) and High BMI (BMI >40 kg/m²). All patients received standardized care, including spinal anesthesia and multimodal analgesia. The primary outcome was the difference in ObsQoR-10 scores between the two groups 24 hours after surgery. Secondary outcomes included pain scores, total opioid consumption, incidence of adverse events, time to first mobilization, length of hospital stay, breastfeeding rates, and readmission rates. RESULTS: Median (interquartile range) ObsQoR-10 scores at 24 hours were 83 (73.5-90.5) versus 82 (70-90) for the control group and the High BMI group, respectively. There were no significant differences in median 24-hour opioid consumption (0, 0 to 10, P=.078) between groups and in the median Numeric Rating Score for pain at 24 hours (0, -1 to 1) at rest (P=0.97) and on movement (P=0.78). There were no differences in length of stay or hospital readmission rates, however patients in the obesity group tended to breastfeed less and mobilize earlier than patients in the control group. CONCLUSION: This study suggests minimal differences in the quality of recovery between the two groups. Future studies should examine recovery in patients with BMI >50, beyond 24 hours, and post-discharge.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".