Study of the Role of Nova Bone as a Filling Material in Cervical Cage in Anterior Fusion of Cervical Spine in Patients with Degenerative Cervical Disc Disease
Bibliographic record
Abstract
Objectives: There are several reports about the therapeutic effects of cervical interbody cages for cervicaldegenerative disorders. Few have addressed the role of the filling material in fusion and improving the clinicalsymptoms. This study tries to study the effects of Novabone as a filling material in Solis cages in patients withcervical disc protrusions. The results have been compared with the results from studies that used autografts to fillthe cage.Material & Methods: This study includes patients treated surgically from 2003 to 2007 for cervical discopathywith anterior fusion technique. We used Solis cage filled with Novabone mixed with autologous bone fragments.The results for fusion and improvement in axial and radicular pain have been determined and compared with theresults of other studies. Also the complications and radiologic findings have been reviewed.Results: 33 patients were operated on from 2003-2007, anteriorly using Solis cages filled with Novabone. 19were men (57.6%) and 14 women (42.4%). The average age was 47.1 y. 23 patients (69.7%) have been operatedon one level and 10 patients (30.3%) were operated on more than one level. 44 cages were inserted. Mean followup period was 14.3 months. After one year the fusion rate was 91.3% at one level and 80% in patients with morethan one level and 87.8% overall.Conclusion: According to our study, the fusion rate with Novabone and autologous bone filled cages were lowerthan that achieved in series using autologous bone but it reached the same rate later. It is highly recommendedthat the cervical cages be filled with autologous bone or Novabone mixed with patints bone chips.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".