COVID-19 Impact on Diagnosis and Staging of Colorectal Cancer: A Single Tertiary Canadian Oncology Center Experience
Bibliographic record
Abstract
Background: Public health measures have imposed drastic reductions in cancer screening programs at the beginning of the COVID-19 pandemic, with an unknown impact on the diagnosis and staging of colorectal cancer (CRC). Methods: Newly diagnosed CRC cases at the Centre Hospitalier de l’Université de Montréal (CHUM) were divided into two groups according to the timeline: pre-pandemic (1 January 2018–12 March 2020), and pandemic (13 March 2020–30 June 2021) periods. Colonoscopy, surgery, and staging at diagnosis during the pandemic period were compared to the pre-pandemic period. Results: 254 CRC diagnoses were made during the pre-pandemic period in comparison to 125 during the pandemic period. Mean diagnosis rates were lower in the pandemic period (7.8 vs. 9.8 diagnoses/month, p = 0.048). Colonoscopy deadlines were less respected in the pandemic period (51.7% vs. 38.3%, p = 0.049). The rate of elective surgery did not differ (2.9 vs. 3.5 surgeries/month, p = 0.39) and mean delays were similar (58.6 vs. 60.4 days, p = 0.77). Stages at diagnosis did not differ (p = 0.17). Most of the delayed colonoscopies led to a stage 0 or I CRC (p = 0.2). Conclusion: In our center, the COVID-19 pandemic resulted in a decreased rate of CRC diagnosis and increased endoscopic delays without affecting the rate of advanced stage disease. Delays to surgery were quite similar once the CRC diagnosis was established.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".