The Impact of the Pandemic on the Quality of Colorectal and Anal Cancer Care, and 2-Year Clinical Outcomes
Bibliographic record
Abstract
We undertook a retrospective study to compare the quality of care delivered to a cohort of newly diagnosed adults with colon, rectal or anal cancer during the early phase of COVID-19 (02/20–12/20) relative to the same period in the year prior (the comparator cohort), and examine the impact of the pandemic on 2-year disease progression and all-cause mortality. We observed poorer performance on a number of quality measures, such as approximately three times as many patients in the COVID-19 cohort experienced 30-day post-surgical readmission (10.5% vs. 3.6%; SD:0.27). Despite these differences, we observed no statistically significant adjusted associations between COVID-19 and time to either all-cause mortality (HR: 0.88, 95% CI: 0.61–1.27, p = 0.50) or disease progression (HR: 1.16, 95% CI: 0.82–1.64, p = 0.41). However, there was a substantial reduction in new patient consults during the early phase of COVID-19 (12.2% decrease), which appeared to disproportionally impact patients who traditionally experience sociodemographic disparities in access to care, given that the COVID-19 cohort skewed younger and there were fewer patients from neighborhoods with the highest Housing and Dwelling, ands Age and Labour Force marginalization quintiles. Future work is needed to understand the more downstream effects of COVID-19 related changes on cancer care to inform planning for future disruptions in care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".