Evaluating the impact of the COVID-19 pandemic on cancer screening in a central Canadian province
Bibliographic record
Abstract
We evaluated the impact of COVID-19 on cancer screening in Manitoba, Canada using an interrupted time series (ITS) design and data from Manitoba's population-based, organized cancer screening programs from April 2020 to August 2021. In June 2020 (breast screening was suspended during April and May 2020), there was a 54% decrease between the predicted (i.e., observed data produced from regression models) and expected (i.e., counterfactual values produced for the COVID-19 period by assuming COVID-19 did not occur) number of screening mammograms (ratio = 0.46, 95% Confidence Interval (CI) 0.28-0.64). By December 2020, there was no significant difference between predicted and expected number of screening mammograms (ratio = 0.95, 95% CI 0.80-1.10). In April 2020, there was an 83% decrease in the number of Pap tests (ratio = 0.17, 95% CI 0.04-0.30). By January 2021, there was no significant difference between predicted and expected number of Pap tests (ratio = 0.93, 95% CI 0.81-1.06). In April 2020, there was an 81% decrease in the number of screening program fecal occult blood tests (FOBTs) (ratio = 0.19, 95% CI 0.0-0.44). By September 2020, there was no significant difference between predicted and expected number of FOBTs (ratio = 0.95, 95% CI 0.65-1.24). The estimated cumulative deficit (i.e., backlog) from April 2020 to August 2021 was 17,370 screening mammograms, 22,086 Pap tests, and 5253 screening program FOBTs. Overall, screening programs adapted quickly to the COVID-19 pandemic. Additional strategies may be needed to address remaining backlogs.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".