184 Use of Neuropathic Pain Questionnaires in Predicting the Development of Failed Back Surgery Syndrome Following Lumbar Discectomy for Radiculopathy
Bibliographic record
Abstract
INTRODUCTION: Failed back surgery syndrome (FBSS) is a type of neuropathic pain where extremity symptoms persist despite structurally corrective spinal surgery. This implies more substantial nerve damage rather than simply dysfunction whereby correcting the inciting structural derangement does not provide for clinical resolution. What remains unclear is how to predict which patients are likely to derive benefit from surgical intervention, and whether specific pain characteristics are associated with different likelihoods of good outcome. METHODS: This study analyzed a prospective data set of patients managed surgically for painful radiculopathy secondary to lumbar degenerative spondylosis by the senior author at Toronto Western Hospital neurosurgery over the past 24 months. Clinical parameters include general demographics, preoperative and postoperative clinical examination, surgical details, general self-reported pain and disability scores, and neuropathic pain scores. The screening tests used in this study were Douleur Neuropathique 4 (DN4) and Leeds Assessment of Neuropathic Symptoms and Signs (LANSS), correlation tested by the Spearman coefficient. Multiple logistic regression analysis was used to define predictors of persistent postoperative symptomatology. RESULTS: Among 250 patients treated for lumbar radiculopathy, 12% exhibited FBSS with modest relief of leg pain compared with the cohort of patients in whom substantial improvement was seen. The postoperative condition was associated with abnormal preoperative screens for neuropathic pain, but not sex, smoking status, or preoperative pain severity. Good correlation was seen between the 2 screening tests used in this study for both absolute ordinal score and thresholding as neuropathic pain (Spearman, P < .001). CONCLUSION: While FBSS was more common among younger and female patients, this occurred with low overall frequency. Higher neuropathic pain screening scores correlated strongly with the likelihood and severity of significant postoperative leg pain. Further work is required to develop more accurate prognostication tools for patients undergoing structural spinal surgery for lumbar radiculopathy.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".