Dysphagia rates in single- and multiple-level anterior cervical discectomy and fusion surgery: a meta-analysis
Bibliographic record
Abstract
Background: To conduct a meta-analysis to assess dysphagia complicating single-level and multiple-level (≥2) anterior cervical discectomy and fusion (ACDF) surgery. Methods: Electronic searches were performed using four electronic databases from their inception to December 2017. Relevant studies reporting the rate of dysphagia as an endpoint for patients undergoing ACDF for degenerative disease, myelopathy, cervical canal stenosis or ossification of the posterior longitudinal ligament were identified according to prior inclusion and exclusion criteria. Statistical analysis was performed using a fixed effect model. P-scores were used to rank the levels of ACDF based on the rate of dysphagia. I2 was used to explore heterogeneity. Results: Ten studies were identified and included in the systematic review and meta-analysis, with a total of 4,018 patients identified; 2,362 patients underwent single-level ACDF, while 1,656 underwent multiple level (≥2 ACDF). The mean age ranged from 49.45 to 57.77 years. Mean follow-up time ranged from 2 days to 27.3 months. Overall, meta-analysis demonstrated a statistically significant higher dysphagia rate for multiple-level ACDF (6.6%) than for single-level ACDF (4%) (P heterogeneity =0.151, OR =1.42, 95% CI: 1.05–1.91, I2=32%). Conclusions: Dysphagia is a relatively common complication in the early postoperative period following ACDF and may cause patients significant discomfort and distress. This meta-analysis demonstrates a higher rate of dysphagia with multiple-level ACDF than with single-level ACDF at a period of 12–24 months.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.065 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".