Quality of colorectal and anal cancer care delivered during the COVID pandemic.
Bibliographic record
Abstract
432 Background: Reports suggest that there have been significant impacts to the provision of cancer care with delays, interruptions and cancellations across all treatment modalities as a result of the COVID pandemic which have implications for the quality of care. We evaluated the impact of the early phase of the pandemic relative to the same period in the year prior using a panel of measures spanning the six domains of quality; 8 measures focused on pandemic-specific care modifications, and 16 established measures. Methods: The cohort consisted of all new patient consultations between 02/19 to 12/19 (comparator) or 02/20 to 12/20 (COVID) at Princess Margaret Cancer Centre (PM) in Toronto, Canada, who were >18 years of age and newly diagnosed with colon, rectal or anal cancer. Chart abstraction data was linked to Census and the Ontario Marginalization Index datasets to derive additional population-weighted sociodemographic variables. A summary quality score across established measures was computed and a benchmark was set using the pared mean approach representing the top 10% of performers. Associations between achieving the quality benchmark and patient characteristics were evaluated using a multivariable logistic regression model. Results: Relative to the year prior, there was a 12.2% reduction new patient consultations (294 vs 335). Significant findings for individual indicators are summarized below. Relative to English-first language patients, those whose first language was not English were 7.4 times less likely to achieve the benchmark (OR 0.13; 95% CI 0.01-0.61). Compared to those with stage I disease, patients with stage IV disease at diagnosis were 6.5 times less likely to achieve the benchmark (OR: 0.15; 95% CI 0.02-0.78). Conclusions: While overall quality of care was poorer during the early phase of the pandemic, there was a reduction in the proportion of patients treated with systemic therapy within 30 days of death. This likely reflects efforts to prioritize fitter patients with curative disease for treatment and reduce avoidable healthcare utilization. Future work should focus on understanding the downstream impacts of these differences in care quality on clinical outcomes and optimizing preparedness for future disasters.[Table: see text]
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".