Comparison of laminectomy and fusion vs laminoplasty in the treatment of multilevel cervical spondylotic myelopathy
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to evaluate the clinical safety and efficacy between laminectomy and fusion (LF) versus laminoplasty (LP) for the treatment of multi-level cervical spondylotic myelopathy (CSM). METHODS: The authors searched electronic databases using PubMed, MEDLINE, Embase, Cochrane Controlled Trial Register, and Google Scholar for relevant studies that compared the clinical effectiveness of LF and LP for the treatment of patients with multilevel CSM. The following outcome measures were extracted: the Japanese Orthopaedic Association (JOA) scores, cervical curvature index (CCI), visual analog scale (VAS), Nurich grade, reoperation rate, complications, rate of nerve palsies. Newcastle Ottawa Quality Assessment Scale (NOQAS) was used to evaluate the quality of each study. Data analysis was conducted with RevMan 5.3. RESULTS: A total of 14 studies were included in our meta-analysis. No significant difference was observed in terms of postoperative Japanese Orthopaedic Association score (P = .29), visual analog scale neck pain (P = .64), cervical curvature index (P = .24), Nurich grade (P = .16) and reoperation rate (P = .21) between LF and LP groups. Compared with LP group, the total complication rate (OR 2.60, 95% CI 1.85, 3.64, I = 26%, P < .00001) and rate of nerve palsies (OR 3.18, 95% CI 1.66, 6.11, I = 47%, P = .0005) was higher in the LF group. CONCLUSIONS: Our meta-analysis reveals that surgical treatments of multilevel CSM are similar in terms of most clinical outcomes using LF and LP. However, LP was found to be superior than LF in terms of nerve palsy complications. This requires further validation and investigation in larger sample-size prospective and randomized studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".