Analysing the colorectal cancer screening patterns during the COVID-19 pandemic and their effects on patient outcomes.
Bibliographic record
Abstract
40 Background: In Canada, colorectal cancer (CRC) ranks as the third most prevalent cancer and second leading cause of cancer mortality (1). The potential to prevent a significant proportion of colorectal cancers and associated mortality through effective screening is widely acknowledged (Han-Mo Chiu, 2021). Notably, fecal immunohistochemistry testing (FIT) has led to improved early detection of CRC (2). However, FIT screening was suspended in Canada in the initial phase of the COVID-19 pandemic response. Specifically, in Ontario, the collection of FIT samples was suspended between March 23rd, 2020 and August 26th, 2020 (3). During this 3-month period, an estimated 540,000 Canadians would have participated in screening, as indicated by OncoSim, a microsimulation model for cancer (4). The primary objective was to evaluate the consequences of screening cessation, through comparison of asymptomatic and symptomatic CRC diagnoses at the Windsor Regional Cancer Centre in Windsor, Ontario. Methods: A retrospective chart review was performed for patients admitted to the Windsor Regional Cancer Centre of Windsor Regional Hospital between December 2016 and June 2021. Demographic data, risk factors for CRC, cancer operability status, and the presence of symptoms at diagnosis of CRC were recorded. Results: 771 patient charts were reviewed and 77 (10%) were excluded due to duplication or insufficient chart data. Of the remaining 694 patients, 545 (79%) were classified as the pre-COVID group (December 2016 to February 2020) and 149 (21%) as the COVID group (March 2020 to June 2021). We found a 2.5% increase in symptomatic diagnoses of CRC in patients diagnosed after March 2020 (pre-COVID group vs. COVID group). However, we did not observe a significant difference in proportion of inoperable CRC cases between groups. Conclusions: The observed trend of symptomatic diagnoses of CRC underscores the importance of FIT screening for early CRC detection and the need for increased catch-up screening to mitigate the potential risks and mortality associated with CRC screening interruptions. For this, it is presumed that a population of patients exists who would have tested positive through FIT screening but instead presented symptomatically at a later stage of the disease. It is also important to note that our study data did not extend beyond June 2021, so we could not capture the complete impact of FIT cessation. (1) Canadian Cancer Statistics, 2021. (2) Zauber, 2015. (3) Ontario Health, 2021. (4) StatCan, 2021.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".