Predictors of Recovery Following Lumbar Microdiscectomy for Sciatica: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Chronic post-surgical pain is reported by up to 40% of patients after lumbar microdiscectomy for sciatica, a complaint associated with disability and loss of productivity. We conducted a systematic review of observational studies to explore factors associated with persistent leg pain and impairments after microdiscectomy for sciatica. We searched eligible studies in MEDLINE, Embase, and CINAHL that explored, in an adjusted model, predictors of persistent leg pain, physical impairment, or failure to return to work after microdiscectomy for sciatica. When possible, we pooled estimates of association using random-effects models using the Grading of Recommendations Assessment, Development, and Evaluation approach. Moderate-certainty evidence showed that the female sex probably has a small association with persistent post-surgical leg pain (odds ratio (OR) = 1.15, 95% confidence interval (CI) = 0.63 to 2.08; absolute risk increase (ARI) = 1.8%, 95% CI = -4.7% to 11.3%), large association with failure to return to work (OR = 2.79, 95% CI = 1.27 to 6.17; ARI = 10.6%, 95% CI = 1.8% to 25.2%), and older age is probably associated with greater postoperative disability (β = 1.47 points on the 100-point Oswestry Disability Index for every 10-year increase from age (>/=18 years), 95% CI = -4.14 to 7.28). Among factors that were not possible to pool, two factors showed promise for future study, namely, legal representation and preoperative opioid use, which showed large associations with worse outcomes after surgery. The moderate-certainty evidence showed female sex is probably associated with persistent leg pain and failure to return to work and that older age is probably associated with greater post-surgical impairment after a microdiscectomy. Future research should explore the association between legal representation and preoperative opioid use with persistent pain and impairment after microdiscectomy for sciatica.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".