Neuropathic pain after sarcoma surgery
Bibliographic record
Abstract
Surgery for sarcoma frequently causes nerve damage as the dissection often violates the internervous plane. Nerve damage may cause neuropathic pain (NP), which can result in persistent pain after surgery. This is the first study to investigate the prevalence and associated factors of postoperative NP in patients who underwent surgery for sarcoma of the extremities or pelvis.Patients (n = 144) who underwent curative surgery at least 6 months prior to the visit for histologically confirmed sarcoma were enrolled. The presence of NP was assessed by administering PainDetect, a widely used questionnaire for detecting NP. Patients with PainDetect scores ≥13 were considered to have NP. The possible factors that might be associated with the development of NP were investigated: patient characteristics, tumor characteristics, extent of surgery, and adjuvant therapy.Out of 144 patients, 36 patients (25%) had NP. Patients with NP had significantly worse visual analog scale score (P < .001), Toronto Extremity Salvage Score (P < .001), and Musculoskeletal Tumor Society Rating Scale score (P < .001) than patients without NP. Among the possible factors associated with NP, patients with NP were more likely to have undergone pelvic surgery (P = .002) and multiple surgeries (P = .014) than patients without NP. In logistic regression analysis, pelvic surgery (odds ratio = 5.05, P = .005) and multiple surgeries (odds ratio = 2.33, P = .038) were independent factors associated with NP after sarcoma surgery.This study suggests that the prevalence of NP after surgery for sarcoma is considerable. Surgery of the pelvis and multiple surgeries are predictive of postoperative persistent NP.
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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 | 0.002 |
| 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 | 0.000 |
| 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".