Cancer surgery cancellation: incidence, outcomes and recovery in a universal health care system
Bibliographic record
Abstract
<h3>Background:</h3> Cancer surgery cancellation can have negative consequences for the patient, the surgeon and the health care system. There is a paucity of literature on cancer surgery cancellation and its association with wait times, perioperative outcomes, survival and costs of care. Therefore, the objective of this study was to determine the incidence of same-day cancer surgery cancellation in a universal health care context and its association with short and long-term outcomes. <h3>Methods:</h3> This was a population-based retrospective cancer cohort study in Ontario, Canada (2010–2016). There were 199 599 patients in the control cohort and 3539 patients in the cohort that experienced a cancellation. We assessed the cohorts for differences in survival, perioperative complications and costs of care. <h3>Results:</h3> The overall cancellation rate was 1.74% and was predicted by cancer type (genitourinary), lower income quintile, and more central region of residence. Wait times in the cancelled cohort were longer than in the control cohort; however, this difference was not associated with worse survival outcomes. Patients in the cancelled cohort had higher complication rates while in hospital (7.3 %) than those in the control cohort (4.9%; <i>p</i> < 0.01). After adjusting for important confounders, the cancelled cohort was more costly ($1100). <h3>Conclusion:</h3> Same-day cancer surgery cancellation rates were low. They were associated with longer wait times, higher complication rates and increased costs of care. Survival was not worse in the cancelled cohort, suggesting that appropriate cancer urgency prioritization occurs. Preventable causes of cancellation should be targeted to improve outcomes in patients with cancer.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".