Out-of-hours emergent surgery for degenerative spinal disease in Canada: a retrospective cohort study from a national registry
Bibliographic record
Abstract
Background: Spinal degenerative disease represents a growing burden on our healthcare system, yet little is known about longitudinal trends in access and care. Our goal was to provide an essential portrait of surgical volume trends for degenerative spinal pathologies within Canada. Methods: (CIHI) database was used to identify all patients receiving surgery for a degenerative spinal condition from 2006 to 2019. Trends in number of interventions, unscheduled vs scheduled hospitalizations, in-hours vs out-of-hours interventions, resource utilization and adverse events were analyzed retrospectively using linear regression models. Confidence intervals were reported in the expected count ratio scale (CR). Findings: A total of 338,629 spinal interventions and 256,360 hospitalizations between 2006 and 2019 were analyzed. The mean and SD of the annual mean age of patients was 55.5 (SD 1.6) for elective hospitalizations and 55.6 (SD 1.6) for emergent hospitalizations. The proportion of female patients was 47.8% (91,789/192,027) for elective hospitalizations and 41.4% (26,633/64,333) for emergent hospitalizations. Elective hospitalizations increased an average of 2.0% per year, with CR = 1.020 (95% CI 1.017-1.023, p < 0.0001) while emergent hospitalizations exhibited more rapid growth with an average 3.4% annually, with CR 1.034 (95% CI 1.027-1.040, p < 0.0001). «In-hours » surgeries increased on average 2.7% per year, with CR 1.027 (95% CI 1.021-1.033, p < 0.0001), while « out-of-hours » surgeries increased 6.1% annually, with CR 1.061 (95% CI 1.051-1.071, p < 0.0001). The resource utilization for unscheduled hospitalizations approximates two and a half times that of scheduled hospitalizations. The proportions of spinal interventions with at least one adverse event increased on average 6.3% per year, with CR 1.063 (95% CI 1.049-1.077, p < 0.0001). Interpretation: This study provides novel data critical for all providers and stakeholders. The rapid growth of emergent out-of-hours hospitalizations demonstrates that the needs of this growing patient population have far exceeded health-care resource allocations. Future studies will analyze the health-related quality of life implications of this system shift and identify demographic and socioeconomic inequities in access to surgical care. Funding: This work was funded by the Bob and Trish Saunders Spine Research Fund through The VGH and UBC Hospital Foundation. The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".