Impact of the COVID-19 Pandemic on Access to Cancer Surgery: Analysis of Surgical Wait Times in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: As coronavirus disease 2019 (COVID-19) spread, efforts were made to preserve resources for the anticipated surge of COVID-19 patients in British Columbia, Canada. However, the relationship between COVID-19 hospitalizations and access to cancer surgery is unclear. In this project, we analyze the impact of COVID-19 patient volumes on wait time for cancer surgery. METHODS: We conducted a retrospective study using population-based datasets of regional surgical wait times and COVID-19 patient volumes. Weekly median wait times for urgent, nonurgent, cancer, and noncancer surgeries, and maximum volumes of hospitalized patients with COVID-19 were studied. The results were qualitatively analyzed. RESULTS: A sustained association between weekly median wait time for priority and other cancer surgeries and increase hospital COVID-19 patient volumes was not qualitatively discernable. In response to the first phase of COVID-19 patient volumes, relative to pre-COVID-19 pandemic levels, wait time were shortened for urgent cancer surgery but increased for nonurgent surgeries. During the second phase, for all diagnostic groups, wait times returned to pre-COVID-19 pandemic levels. During the third phase, wait times for all surgeries increased. CONCLUSION: Cancer surgery access may have been influenced by other factors, such as policy directives and local resource issues, independent of hospitalized COVID-19 patient volumes. The initial access limitations gradually improved with provincial and institutional resilience, and vaccine rollout.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| 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) | 0.001 | 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".