Incisional Hernia After Midline Versus Transverse Specimen Extraction Incision
Bibliographic record
Abstract
OBJECTIVE: To compare the incidence of incisional hernia (IH) between midline and transverse specimen extraction site in patients undergoing laparoscopic colectomy. BACKGROUND: Midline specimen extraction incision is most commonly used in laparoscopic colectomy, but has high IH risk. IH may be lower for transverse incision. METHODS: A single-center superiority trial was conducted. Eligible patients undergoing laparoscopic colectomy were randomly assigned to midline or transverse specimen extraction. Primary outcome was IH incidence at 1 year. Power calculation required 76 patients per group to detect a reduction in IH from 20% to 5%. Secondary outcomes included perioperative outcomes, pain scores, health-related quality of life (SF-36), and cosmesis (Body Image Questionnaire). RESULTS: A total of 165 patients were randomly assigned to transverse (n = 79) or midline (n = 86) specimen extraction site, of which 141 completed 1-year follow-up (68 transverse, 73 midline). Patient, tumor, surgical data, and perioperative morbidity were similar. Pain scores were similar on each postoperative day. On intention-to-treat analysis, there was no difference in the incidence of IH at 1 year (transverse 2% vs midline 8%, P = 0.065) or after mean 30.3 month (standard deviation 9.4) follow-up (6% vs 14%, P = 0.121). On per-protocol analysis there were more IH after midline incision with longer follow-up (15% vs 2%, P = 0.013). On intention-to-treat analysis, SF-36 domains body pain and social functioning were improved after transverse incision. Cosmesis was higher after midline incision on per-protocol analysis, but without affecting body image. CONCLUSIONS: Per-protocol analysis of this trial demonstrates that a transverse specimen extraction site has a lower incidence of IH compared to midline with longer follow-up but has worse cosmesis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".