A Canadian perspective on anterior cervical discectomies: practice patterns and preferences
Bibliographic record
Abstract
Background: The purpose of this study is to elucidate the current practice patterns of Canadian neurosurgeons with regards to anterior cervical discectomy (ACD). Methods: A one-page questionnaire was sent out using SurveyMonkey to all neurosurgeon members of the Canadian Neurological Sciences Federation (CNSF). End points were surgeon preference for ACD surgical method, graft source, the length of collar usage and the recommended time before returning to work. Results: Response rate was 74.0%. Of the responders, 75.0% performed single level ACD and 18.3% had completed spine fellowships. The majority (68.2%) chose ACD with fusion and plating (ACDFP) as their preferred method with allograft being the most popular choice of fusion material (44.3%). Most of the respondents did not prescribe collars (60.9%) and when they did, hard collar was prescribed most often (76.9%) and AspenTM collar was the most popular choice (67.7%). The majority of surgeons chose ‘other’ as their response for length of time for collar use (40.0%) while allowing them to take their collars off at night (78.1%). Most of the surgeons recommended physiotherapy post-operatively (58.1%) and time to physiotherapy was 6–8 weeks. Recommended back to work time was 6 weeks for 44.9% of respondents. In the cross analysis, surgeons who performed ACDF were more likely to prescribe collars (50%, P=0.01) versus surgeons who performed ACDFP (21.7%, P=0.01). Conclusions: Our survey is an up to date description of current practice patterns for ACD amongst Canadian neurosurgeons.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".