When can lumbar fusion be considered appropriate in the treatment of recurrent lumbar disc herniation? A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Recurrent lumbar disc herniation (RLDH) is defined as the reappearance, following initial discectomy, of disc material and pain after a period of at least six symptom-free months. Redo surgery is usually considered following unsuccessful conservative management or in the presence of neurological deficits. Research question: Given the lack of consensus on the ideal surgical strategy for RLDH, we conducted this study to evaluate when lumbar fusion (LF) should be considered in the treatment of RLDH. Material and methods: A literature search was conducted on PubMed, Google Scholar and clinicaltrials.gov focusing on the treatment of recurrent disc herniation using microdiscectomy alone or through fusion. The quality of the studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale and Cochrane Risk of Bias Tool 2.0. The weighted mean difference was calculated for both binary and continuous outcomes. Results: This resulted in a list of 900 references, from which 11 studies were identified as meeting the inclusion criteria for the study. There were four prospective studies and seven retrospective studies. A comparison of LF and redo discectomy (RD) revealed no significant differences in clinical outcome scores. LF resulted in significantly higher intraoperative blood loss, longer hospitalizations and longer surgeries. No further differences were identified. Discussion and conclusions: Both LF and RD represent safe and effective treatment options in first RLDH. The choice of surgical strategy should integrate the eventual co-existence of clinical and radiological features of segmental instability, as well subjective aspects, such as surgeons' training and patient preference.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".