Lumbar Discectomy
Bibliographic record
Abstract
STUDY DESIGN: We followed a longitudinal observational design with 2 assessment points, presurgery and postsurgery, in 83 consecutive patients undergoing single-level lumbar discectomy. OBJECTIVE: Prognostic data can be gathered from commonly used generic outcome measures to identify patients at risk of persistent leg pain-associated chronicity, following lumbar discectomy SUMMARY OF BACKGROUND DATA:: Suboptimal results observed, following open lumbar discectomy, have been connected to the interplay among presurgery pain characteristics, functional and psychosocial adaptations like persistent pain, disability, and depression. Outcome predictive qualities have been recently attributed to well-known outcome measures. However, most studies on prognostic indicators use multiple tools designs, inhibiting clinical application. Here we elaborate on predictive indications identified in 2 generic patient-rated questionnaires, Short Form-36 (SF-36) and McGill Pain, as many of their domains can evaluate factors related to unfavorable outcomes. METHODS: For the prognostic value calculations, multivariate logistic [Short-Form McGill Pain Questionnaire (SF-MPQ)] and linear regression models (SF-36) were fitted to investigate the association between presurgery and postsurgery scores. In all models, the presurgical score at question was assigned as the dependent variable while age, sex and presurgery score at question were the independent variables. RESULTS: Overall, a statistically significant amelioration in both SF-MPQ and SF-36 scores was observed postsurgically. For the SF-MPQ leg cramping, gnawing, burning, and aching pain symptoms, when present presurgically, were the least responsive to treatment. For the SF-36, mental scores overall were less responsive than physical equivalents postoperatively, while general health perception improved only marginally. Differences in pain level scores did not correlate with an equivalent reduction in postsurgery anxiety and depression indices. CONCLUSIONS: SF-MPQ and SF-36 can assist in treatment decision, as they can readily identify patients at risk of unfavorable outcomes even in primary/clinical settings. The above findings additionally suggest a wider scope of clinical use for the above questionnaires allowing parallel processing and interpretation of the same patient data. LEVELS OF EVIDENCE: Level I.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".