Risk Factors of Post-Surgical Neuropathic Pain in Cesarean Section Patients A Population-based Study
Bibliographic record
Abstract
Background: We elucidated the incidence and the risk factors of post-surgical neuropathic pain (PSNP) in Cesarean section (CS) patients. Methods: Data from the Taiwan Longitudinal Health Insurance Database were analyzed. CS patients with a primary diagnosis of PSNP and had at least two ambulatory visits for PSNP treatments were identified as the PSNP subjects. CS patients without PSNP diagnosis were identified as the non-PSNP subjects. The PSNP and non-PSNP subjects were tracked from the surgery date until the end of 2008 or until loss of follow-up. Subjects with a previous history of PSNP before CS, age less than 16 or more than 50 years or missing data of anesthetic mode were excluded. Results: A total of 85 PSNP subjects and 11284 non-PSNP subjects were included. Multivariate logistic regression analyses revealed that diabetes mellitus was a risk factor of PNSP in CS patients [odds ratio (OR)=27, 95% confidence intervals (CI): 15.73 - 46.36; P＜O.OOI]. The other risk factors of PSNP in CS patients included age ≧ 35 years (OR=3.25, 95% CI: 2.01. 5.28; P＜0.001), hypertension (OR=2.48, 95% CI: 1.12 - 5.50; P=0.026), hyperlipidemia (OR=3.08, 95% CI: 1.29 - 7.39; P=0.012), chronic alcohol exposure (OR=9.61 , 95% CI: 1.53 - 60.27; P=0.016), smoking (OR=3.09, 95% CI: 1.54 - 6.21; P=0.001) and use of antidepressants (OR=7.40, 95% CI: 3.56 - 15.37; P＜0.001). Conclusions: The incidence of PSNP in CS patients was 0.74% and the risk factors included DM, age ≧ 35 years, hypertension, hyperlipidemia, chronic alcohol exposure, smoking and use of antidepressants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".