Long-Term Survivorship of Cervical Spine Procedures; A Survivorship Meta-Analysis and Meta-Regression
Bibliographic record
Abstract
STUDY DESIGN: Systematic Review. OBJECTIVES: To conduct a meta-analysis on the survivorship of commonly performed cervical spine procedures to develop survival function curves for (i) second surgery at any cervical level, and (ii) adjacent level surgery. METHODS: A systematic review of was conducted following PRISMA guidelines. Articles with cohorts of greater than 20 patients followed for a minimum of 36 months and with available survival data were included. Procedures included were anterior cervical discectomy and fusion (ACDF), cervical disc arthroplasty (ADR), laminoplasty (LAMP), and posterior laminectomy and fusion (PDIF). Reconstructed individual patient data were pooled across studies using parametric Bayesian survival meta-regression. RESULTS: Of 1829 initial titles, 16 citations were included for analysis. 73 811 patients were included in the second surgery analysis and 2858 patients in the adjacent level surgery analysis. We fit a Log normal accelerated failure time model to the second surgery data and a Gompertz proportional hazards model to the adjacent level surgery data. Relative to ACDF, the risk of second surgery was higher with ADR and PDIF with acceleration factors 1.73 (95% CrI: 1.04, 2.80) and 1.35 (95% CrI: 1.25, 1.46) respectively. Relative to ACDF, the risk of second surgery was lower with LAMP with deceleration factor .06 (95% CrI: .05, .07). ADR decreased the risk of adjacent level surgery with hazard ratio .43 (95% CrI: .33, .55). CONCLUSIONS: In cases of clinical equipoise between fusion procedures, our analysis suggests superior survivorship with anterior procedures. For all procedures, laminoplasty demonstrated superior survivorship.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.059 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".