Descriptive Data Analysis on the Effect of General Anesthesia Versus Epidural Anesthesia in Postoperative Patients Regarding Pain
Bibliographic record
Abstract
This study aims to investigate the impact of different anesthesia techniques, specifically regional anesthesia (RA) and general anesthesia (GEA), on postoperative pain, recovery, and overall experience in women undergoing cesarean sections. The primary objective is to compare pain levels and analgesic needs between RA and GEA, using tools like the Short-Form McGill Pain Questionnaire (SF–MPQ), Visual Analog Scale (VAS), and the Pain Quality Scale. Additionally, the study will assess recovery outcomes, including the time to first independent mobilization and the onset of lactation, alongside the emotional and psychological effects of each anesthesia method. A sample of 120-150 patients, selected via convenience sampling from private hospitals, will complete a questionnaire designed to collect both quantitative data (pain levels, mobilization time) and qualitative data (emotional experiences, satisfaction). The study hypothesizes that RA will result in lower pain levels and reduced analgesic consumption compared to GEA, as well as faster recovery, including quicker mobilization and lactation. The findings aim to provide valuable insights into optimizing postoperative care and anesthesia choices for cesarean deliveries, potentially improving patient outcomes and guiding clinical practices.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".