Negative impact of COVID-19 associated health system shutdown on patients diagnosed with colorectal cancer: a retrospective study from a large tertiary center in Ontario, Canada
Bibliographic record
Abstract
Abstract Background In March 2020, a directive to halt all elective and non-urgent procedures was issued in Ontario, Canada because of COVID-19. The directive caused a temporary slowdown of screening programs including surveillance colonoscopies for colorectal cancer (CRC). Our goal was to determine if there was a difference in patient and tumour characteristics between CRC patients treated surgically prior to the COVID-19 directive compared to CRC patients treated after the slowdown. Methods CRC resections collected within the Champlain catchment area of eastern Ontario in the six months prior to COVID-19 (August 1, 2019-January 31, 2020) were compared to CRC resections collected in the six months post-COVID-19 slowdown (August 1, 2020-January 31, 2021). Clinical (e.g. gender, patient age, tumour site, clinical presentation) and pathological (tumour size, tumour stage, nodal stage, lymphovascular invasion) features were evaluated using chi square tests, T-tests and Mann-Whitney tests where appropriate. Results 343 CRC specimens were identified (175 pre-COVID-19, 168 post-COVID-19 slowdown). CRC patients treated surgically post-COVID-19 slowdown had larger tumours (44 mm vs. 35 mm; p = 0.0048) and were more likely to have presented emergently (24% vs .10%; p < 0.001). While there was a trend towards higher tumour stage, nodal stage, and clinical stage, these differences did not reach statistical significance. Other demographic and pathologic variables including patient gender, age, and tumour site were similar between the two cohorts. Interpretation The COVID-19 slowdown resulted in a shift in the severity of disease experienced by CRC patients in Ontario. Pandemic planning in the future should consider the long-term consequences to cancer diagnosis and management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".