Evaluation of fusion in a PEEK cage after anterior cervical discectomy and fusion
Bibliographic record
Abstract
Anterior Cervical Discectomy and Fusion (ACDF) is the gold standard treatment for cervical spondylosis but there is a lack of consensus in the literature regarding which type of bone graft is superior: autograft or allograft. The purpose of this study is to evaluate fusion after ACDF using a stand-alone intervertebral cage packed with autologous cervical bone shavings acquired during the procedure. Twenty patients that underwent single-level ACDF from 2011 to 2014 using a stand-alone polyetheretherketone (PEEK) cage were recruited. Patients were evaluated for evidence of bone fusion by plain films and CT scan. Fusion was primarily assessed by grading the level of trabecular bridging bone across the bone-graft interface. Odom’s criteria were used to assess clinical outcome. All interbody disc spaces achieved successful fusion at follow-up. A total of 80% (16/20) of patients had radiographic evidence of trabecular bone present both within and around the cage. The other 20% exhibited bridging bone within the cage but had evidence of minor radiolucent gaps and lack of bridging bone completely surrounding the cage. Eighty percent of patients reported excellent/good clinical outcomes. ACDF using a PEEK stand-alone cage with autograft bone shavings has a high rate of fusion and avoids potential complications of classic autograft harvesting and decreased allograft fusion rates.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".