Evaluating the impact of <scp>COVID</scp>‐19 on cancer declarations in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: COVID-19 affected healthcare worldwide, limited access to healthcare, and delayed cancer screening and diagnosis. In this study, the effect of the first year of COVID-19 was determined on cancer diagnoses in the province of Quebec, Canada. METHODS: Data were collected from the 13 Quebec Cancer Registry health institutions. Newly diagnosed cancer declarations in the first year of the COVID-19 (April 2020-March 2021) were compared with the reference periods (averages of 3 previous years). The main focus was on four leading cancers: lung, prostate, colorectal, and breast cancers. Generalized regression models with a poisson approximation and interrupted time series (ITS) analysis were used. Underestimated cases were presented in terms of relative risk (RR) and 95% confidence intervals (CI). The changes in the stage-specific counts were also assessed in each of the four cancers. Results were illustrated separately for the first 4 months of the pandemic (first wave). FINDINGS: This study estimated an overall under-reporting of 15.3% (29,019 vs. 24,584) of declarations. This under-reporting was evident across all age groups above 35 years (p < 0.0001), four primary cancers (p < 0.0001), all stages of cancers (p < 0.0001), and both sexes (p < 0.0001). Based on the relative risks, stage-specific lung cancer counts were underestimated by 5%-34% in the first wave (0%-11% in the first year), prostate cancer by 16%-46% in the first wave (0%-25% in the first year), colorectal cancer 15%-45% in the first wave (0%-24% in the first year), and breast cancer 3%-45% in the first wave and (0%-28% in the first year). However, no stage-IV cancers were statically under-reported compared to the pre-pandemic era and not even in the first wave. INTERPRETATION: Cancer diagnosis was underestimated due to the COVID-19 pandemic in the first year; this effect was more evident in the first phase of the pandemic in Quebec. Further research is required to determine the accurate burden of the disease in the long term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 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".