A Cohort Study Analysing the Impact of the COVID-19 Pandemic on Colorectal Cancer Presentations in a Medium-Large Canadian Community Hospital
Bibliographic record
Abstract
Introduction Colorectal Cancer (CrC) is a common cause of cancer-related death worldwide, but screening programs are highly effective at diagnosing early-stage disease, allowing effective treatment. During COVID-19, a decrease in screening participation was hypothesized due to limited access, leading to an increase in symptomatic presentations and stage at diagnosis. Methods All patients who met inclusion criteria were divided into two cohorts based on time of diagnosis ( n = 373). The pre-COVID era was designated as December 2018 to February of 2020, with the COVID era running from then until March 2021. All patients were from the Windsor Regional Hospital Cancer Centre, located in Windsor, Canada. Results Across time periods, 218 patients were diagnosed prior to, and only 144 during COVID. The number of Fecal Immunochemical Test (FIT) positive patients remained stable, while the number of procedural diagnoses decreased from 34.1% to 10.7%, with only 21.2% of patients overall being diagnosed with screening. When combining time periods, females presented symptomatically (85.0%) more often than males (74.4%). Patients with a positive family history were more likely to be diagnosed via procedural screening (42.9%) than those without (20.4%). Conclusion There was no change to the proportion of symptomatic presentations across time groups, in contrast to our predicted outcome. There was a decrease in procedural screening during the COVID timeframe, with FIT testing rates remaining stable, likely representing patients being transferred to available methods. Female patients and patients with a family history demonstrated a particular need for increased screening participation based on our findings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".