Endplate preparation for anterior cervical discectomy and fusion: does the amount of endplate removed affect cage subsidence risk?
Bibliographic record
Abstract
Abstract Purpose Subsidence after anterior cervical discectomy and fusion (ACDF) is a common complication that may be influenced by the degree of endplate removal prior to cage insertion. The optimal degree of endplate removal remains unclear; therefore, we performed a series of ex vivo experiments to elucidate the relationship between the aggressiveness of endplate preparation and subsidence risk. Methods Human cadaveric subaxial cervical endplates were partially decorticated either conservatively (n = 10) or aggressively (n = 9). The degree of endplate removal was quantified using microCT. Subsidence was modelled by measuring the strength and stiffness of each specimen when an interbody cage was axially compressed into the endplate. Results Conservative endplate preparation resulted in less endplate removal than aggressive endplate preparation (mass: 150 vs. 301 mg, p < 0.001; volume: 47 vs. 88mm3, p = 0.01; thickness: 0.02 vs. 0.16 mm, p = 0.004). There was no significant difference between the two groups with respect to endplate strength (2.04 vs. 2.04kN, p = 0.99) or stiffness (2.38 vs. 2.41kN/mm, p = 0.89). Bone mineral density (BMD) was similar between the two groups (271.6 vs. 271.9 mg/cm3, p = 0.98) but positively correlated with endplate strength (effect size 0.68, p = 0.001). Conclusions When performing partial cervical endplate decortication, the degree of bony endplate removal did not significantly predict endplate integrity during ex vivo compression testing, but greater BMD was associated with increased strength. The degree of endplate removal should be based on individual patient factors and intraoperative findings to achieve the ideal cage-endplate interface.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".