Does surgical treatment increase the progression of spinal cord injury in patients with ossification of posterior longitudinal ligament of cervical spine? A systematic review and meta-analysis
Bibliographic record
Abstract
Purpose:The purpose of this study is to explore if the surgical treatment will accelerate the progression of spinal cord injury (SCI) in patients with cervical Ossification of the posterior longitudinal ligament (OPLL) and if surgery will have better curative effect than conservative treatment.Methods:An extensive search of literature was implemented in PubMed, EMBASE, and other online databases. The quality of the included articles was evaluated according to the Newcastle-Ottawa Quality Assessment Scale, as recommended by the Cochrane manual, and meta-analysis was performed using the review manage5.3 software.Results:No obvious statistical difference was observed in the rate of SCI progression (P > 0.05, OR 1.15 [0.66, 2.00]), cervical range of motion, (P > 0.05, weighted mean difference (WMD) 4.52 [−5.75, 14.79]), and Japanese Orthopedic Association scores before surgery (P > 0.05, WMD −2.78 [−7.87, 2.32]) between the surgical group and conservative treatment group. However the surgical group illustrated obviously higher neurofunctional recovery rate (P < 0.05, OR 6.07 [1.55, 23.78]) and postoperative JOA score of the surgery group (P < 0.05, WMD −0.77 [−1.21, −0.33]) than conservative group.Conclusions:Based on this meta-analysis, there is not enough evidence to indicate that surgery will accelerate the progress of SCI with OPLL. However, the superiority of surgical efficacy can be observed over conservative treatment in terms of relieving neurological symptoms.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".