Use of neuropathic pain questionnaires in predicting persistent postoperative neuropathic pain following lumbar discectomy for radiculopathy
Bibliographic record
Abstract
OBJECT Failed-back surgery syndrome has been historically used to describe extremity neuropathic pain in lumbar disease despite structurally corrective spinal surgery. It is unclear whether specific preoperative pain characteristics can help determine which patients may be susceptible to such postoperative disabling symptoms. METHODS This prospective study analyzed surgical microdiscectomy patients treated for lumbar, degenerative, painful radiculopathy. Clinical parameters included general demographics, preoperative and postoperative clinical examination status, self-reported pain and disability scores, and neuropathic pain scores. The screening tests for neuropathic pain were the Douleur Neuropathique 4 and Leeds Assessment of Neuropathic Symptoms and Signs, with correlation tested for ordinal score and screen positivity. Multiple logistic regression analysis was used to define predictors of postoperative symptomatology. RESULTS Twelve percent of the 250 patients with radiculopathy who underwent microdiscectomy experienced persistent postoperative neuropathic pain (PPNP) with only modest, if any, relief of leg pain. The condition was highly associated with abnormal preoperative screen results for neuropathic pain, but not sex, smoking status, or preoperative pain severity (α = 0.05). Good correlation was seen between the 2 screening tests used in this study for both absolute ordinal score (Spearman ρ = 0.84; p < 0.001) and the threshold for terming the patient as having neuropathic pain features (Spearman ρ = 0.48; p < 0.001). Younger age at treatment also correlated with a higher likelihood of developing PPNP (p = 0.03). CONCLUSIONS This population exhibited a low overall frequency of PPNP. Higher neuropathic pain screening scores correlated strongly with likelihood of significant postoperative leg pain. Further work is required to develop more accurate prognostication tools for radiculopathy patients undergoing structural spinal surgery.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".