Consultation and Surgical Wait Times in Cervical Spondylotic Myelopathy
Bibliographic record
Abstract
BACKGROUND: Cervical spondylotic myelopathy (CSM) is the leading cause of spinal cord impairment. In a public healthcare system, wait times to see spine specialists and eventually access surgical treatment for CSM can be substantial. The goals of this study were to determine consultation wait times (CWT) and surgical wait times (SWT), and identify predictors of wait time length. METHODS: Consecutive patients enrolled in the Canadian Spine Outcomes and Research Network (CSORN) prospective and observational CSM study from March 2015 to July 2017 were included. A data-splitting technique was used to develop and internally validate multivariable models of potential predictors. RESULTS: A CSORN query returned 264 CSM patients for CWT. The median was 46 days. There were 31% mild, 35% moderate, and 33% severe CSM. There was a statistically significant difference in median CWT between moderate and severe groups; 207 patients underwent surgical treatment. Median SWT was 42 days. There was a statistically significant difference in SWT between mild/moderate and severe groups. Short symptom duration, less pain, lower BMI, and lower physical component score of SF-12 were predictive of shorter CWT. Only baseline pain and medication duration were predictive of SWT. Both CWT and SWT were shorter compared to a concurrent cohort of lumbar stenosis patients (p <0.001). CONCLUSIONS: Patients with shorter duration (either symptoms or medication) and less neck pain waited less to see a spine specialist in Canada and to undergo surgical treatment. This study highlights some of the obstacles to overcome in expedited care for this patient population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".