Correlation between Pain Scores and Disc Height Changes after Discectomy in Patients with Lumbar Disc Herniation: A Systematic Review and Meta-Analysis
Post-publication record
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Bibliographic record
Abstract
Background. Surgery can reduce and improve lumbar disc herniation, but some patients still have pain after surgery, and the relationship between lumbar disc height and pain after surgery is still unclear. Objective. The main objective is to investigate the relationship between lumbar disc height and postoperative pain. Methods. We searched Pubmed, Web of Science, the Cochrane library, and Embase online for cohort studies or RCT studies on discectomy and assessed the quality of the included articles using the Newcastle-Ottawa Scale (NOS scale), with disc height (DH) and postoperative back pain as the main clinical outcome indicators, and the correlation coefficient between DH and back pain as the statistic to assess the pooled effect size. Results. 10 kinds of literature were included in this study for quantitative analysis. A total of 589 patients participated in the study. The follow-up time was between 1 and 2.3 years. Meta-analysis showed that after surgery, the relief of back pain was statistically significant (MD = −2.57, 95% CI (−3.10,−2.04), Z = −9.570, P < 0.0001 ), the reduction of disc height was statistically significant (MD = −0.82, 95% CI (−1.11, −0.52), Z = −5.477, P < 0.0001 ), the combined value of correlation coefficient Fisher’s Z value was 0.33, 95% CI (0.25,0.42), with statistical significance ( P < 0.00001 ), suggesting that the degree of back pain after surgery showed a moderate positive correlation with disc height in the short term. Discussion. After discectomy, the degree of pain is relieved, the disc height is reduced, and low back pain in the short term and disc height showed a moderate positive correlation, but the long-term correlation remains to be studied in depth.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".