Neurosurgical Operative Cancellations in Canada: A Multicentre Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Operative cancellations adversely affect patient health and impose resource strain on the healthcare system. Here, our objective was to describe neurosurgical cancellations at five Canadian academic institutions. METHODS: The Canadian Neurosurgery Research Collaborative performed a retrospective cohort study capturing neurosurgical procedure cancellation data at five Canadian academic centres, during the period between January 1, 2014 and December 31, 2018. Demographics, procedure type, reason for cancellation, admission status and case acuity were collected. Cancellation rates were compared on the basis of demographic data, procedural data and between centres. RESULTS: = 0.008). Female patients less frequently experienced procedural cancellation. Elective, outpatient and spine procedures were more often cancelled. Reasons for cancellation included surgeon-related factors (28.2%), cancellation for a higher acuity case (23.9%), patient condition (17.2%), other factors (17.0%), resource availability (7.0%), operating room running late (6.4%) and anaesthesia-related (0.3%). When clustered, the reason for cancellation was patient-related in 17.2%, staffing-related in 28.5% and operational or resource-related in 54.3% of cases. CONCLUSIONS: Neurosurgical operative cancellations were common and most often related to operational or resource-related factors. Elective, outpatient and spine procedures were more often cancelled. These findings highlight areas for optimizing efficiency and targeted quality improvement initiatives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".