RF10 The effectiveness of surgical intervention pertaining to painless foot drop caused by lumbar disc herniation: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Since painless foot drop is an extremely rare presentation of lumbar degenerative disease, there is currently a paucity of evidence on management and outcomes which causes a lack of standardized treatment provided to patients. Our systematic review aimed to determine the effectiveness of surgical intervention concerning conservative management in patients with painless foot drop.Methods A systematic database search was performed across PubMed/MEDLINE and Cochrane Library, between October 2022 and January 2023. Only studies reporting on painless foot drop due to degenerative lumbar disease in adults were included. Foot drop was determined by assessing the Medical Research Council (MRC) power grade of foot dorsiflexion, specifically defined as a Manual Muscle Testing score of 3 or lower were included.Results 578 articles were screened and only 6 met the inclusion criteria. A significant association was demonstrated between the timing of the decompressive surgery (i.e., early decompressions performed better than delayed), MRC grade pre-operatively, and postoperative recovery. Relationships between age at surgery and higher rates of recovery could not be established.Conclusion This is the first systematic review to explore the outcome of surgical versus conservative therapy for painless foot drop. The findings of this systematic review indicate that the duration of foot drop weakness and MRC grade before intervention were strong predictors of surgical outcome.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".