Cesarean Section Incision Length and Post-Operative Pain [34R]
Bibliographic record
Abstract
INTRODUCTION: Cesarean sections are a common surgical procedure and are associated with post-operative pain. The objective of this study is to describe the variability in cesarean section incision length and its association with post-operative pain, scar satisfaction and analgesia use. METHODS: After approval from the Institutional Review Board, we conducted a hospital-based study on women from a tertiary care center's post-partum unit who delivered by cesarean section. Written informed consent was obtained. Post-operative pain was measured using a visual analog scale from 0 to 10 at the first post-operative day. Cesarean section incision length was measured on the second post-operative day. Descriptive analyses were used to describe incision lengths and pain levels. RESULTS: We recruited 107 women having delivered by cesarean section. The mean incision length was 16.39 cm (SD ± 1.78 cm), ranging from 13 to 22 cm, and skewed towards longer incisions, with 20.6% having an incision ≥ 18 cm. The mean pain level on the first post-operative day was 5.7/10 (SD = ± 2.6), 4.4/10 (SD = ± 2.3) on the second day, and 1.9/10 (SD = ± 2.2) at 6 weeks post-partum. There was no significant difference in pain level according to incision length at the first (P=0.224), second (P=0.350), and 6 week (P=0.904) post-operative mark CONCLUSION: There is considerable variability in cesarean delivery incision length. We did not identify any significant predictor for larger incisions. Incision length was found to have no impact on the post-operative pain level at one day, two days or six weeks after cesarean delivery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".