Comparison of Post-Cesarean Pain Perception of General Versus Regional Anesthesia, a Single-Center Study
Bibliographic record
Abstract
Background and Objectives: Pain during and after the procedure remains the leading concern among women undergoing cesarean section. Numerous studies have concluded that the type of anesthesia used during a cesarean section undoubtedly affects the intensity and experience of pain after the operation. Materials and Methods: This prospective cohort study was conducted at the Clinic for Gynecology and Obstetrics, Clinical Center “Dragisa Misovic—Dedinje”, Belgrade, Serbia. Patients at term pregnancy (37–42 weeks of gestation) with an ASA I score who delivered under general (GEA) or regional anesthesia (RA) by cesarean section were included in the study. Following the procedure, we assessed pain using the Serbian McGill questionnaire (SF–MPQ), Visual Analogue Scale (VAS) and the pain attributes questionnaire at pre-established time intervals of 2, 12, and 24 h after the procedure. Additionally, time to patient’s functional recovery was noted. We also recorded the time to the first independent mobilization, first oral intake, and lactation establishment. Results: GEA was performed for 284 deliveries while RA was performed for 249. GEA had significantly higher postoperative sensory and affective pain levels within intervals of 2, 12, and 24 h after cesarean section. GEA had significantly higher postoperative VAS pain levels. On pain attribute scale intensity, GEA had significantly higher postoperative pain levels within all intervals. Patients who received RA had a shorter time to first oral food intake, first independent mobilization, and faster lactation onset in contrast to GEA. Conclusions: The application of RA presented superior postoperative pain relief, resulting in earlier mobilization, shorter time to first oral food intake, and faster lactation onset in contrast to GEA.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".