Risk Factors for Postoperative Subsidence of Single-Level Anterior Cervical Discectomy and Fusion
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To investigate and analyze the preoperative risk factors affecting subsidence after anterior cervical discectomy and fusion (ACDF) to reduce subsidence. SUMMARY OF BACKGROUND DATA: Subsidence after ACDF may be caused by various risk factors, although the related information is scarce. METHODS: Seventy-eight patients who underwent single-level ACDF between 2005 and 2011 were included. Patients were categorized into the subsidence (n = 26) and nonsubsidence groups (n = 52). Preoperative factors such as age, sex, operative level, bone mineral density, cervical alignment, segmental sagittal angle, and anterior/posterior disc height were assessed. The use of plates and the anterior/posterior disc height gap were examined as perioperative factors. The clinical outcome was assessed using a visual analogue scale for neck and arm pain. RESULTS: Subsidence occurred in 26 (33.3%) of 78 patients. A significant difference was found in clinical outcomes between the subsidence and nonsubsidence groups (P < 0.05). The fusion rate was 61.5% in the subsidence group. The mean time to subsidence was 4.8 months. Logistic regression analysis revealed that cervical alignment (P = 0.017), age (P = 0.022), and use of plates (P = 0.041) affected subsidence. In patients who received a stand-alone cage, the risk of subsidence was significantly greater in the kyphotic angle group than in the lordotic angle group (odds ratio = 13.56; P < 0.001). CONCLUSION: After ACDF, the main factors affecting subsidence are cervical alignment, age, and use of plates. Our data suggest that surgeons should consider the kyphotic curvature and/or age when deciding on the use of plates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".