Peripheral Nerve Injury during Abdominal-Pelvic Surgery: Analysis of the National Surgical Quality Improvement Program Database
Bibliographic record
Abstract
Peripheral nerve injury (PNI) is a rare but preventable complication of surgery. We sought to assess whether the use of minimally invasive surgery (MIS) affects the occurrence of PNI. Using the American College of Surgeons National Surgical Quality Improvement Program database, we examined rates of PNI among patients undergoing appendectomy, hysterectomy, colectomy, or radical prostatectomy between 2005 and 2012. We assessed the effect of MIS, as compared with open surgery, on PNI occurrence using logistic regression. Among 297,532 patients, of whom 175,884 (59.1%) underwent MIS, the rate of PNI was 0.03 per cent. Forty-four patients treated using MIS had PNI (0.03%) as compared with 63 who underwent open surgery (0.05%; P = 0.0002). There was a significant decrease in the proportion of surgeries resulting in PNI (P < 0.0001) over time. In univariate analysis, MIS was associated with a decreased occurrence of PNI (odds ratio 0.48, 95% confidence interval 0.33-0.71), but this became nonsignificant on multivariable analysis (odds ratio 0.71, 95% confidence interval 0.47-1.09). Increased operative time and smoking status were the only factors independently associated with an increased risk of PNI on multivariable analysis. MIS techniques during common abdominal-pelvic surgeries do not appear to increase the risk of PNI. Prolonged operative time and smoking are independently associated with an increased risk of PNI. Quality improvement initiatives to increase awareness of PNI and identify patients at increased risk of this preventable complication should be considered.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".