Imaging and physician visits at cancer diagnosis: <scp>COVID</scp> ‐19 pandemic impact on cancer care
Bibliographic record
Abstract
BACKGROUND: Little is known about the COVID-19 pandemic impact on the provision of diagnostic imaging and physician visits at cancer diagnosis. METHODS: We used administrative databases from Ontario, Canada, to identify MRI/CT/ultrasound scans and in-person/virtual physician visits conducted with cancer patients within 91 days around the date of diagnosis in 2016-2020. In separate segmented regression procedures, we assessed the trends in weekly volume of these services per thousand cancer patients in prepandemic (June 26, 2016 to March 14, 2020), the change in mean volume at the start of the pandemic, and the additional change in weekly volume during the pandemic (March 15, 2020, to September 26, 2020). RESULTS: Totally, 403,561 cancer patients were included. On March 15, 2020 (COVID-19 arrived), mean scan volume decreased by 12.3% (95% CI: 6.4%-17.9%) where ultrasound decreased the most by 31.8% (95% CI: 23.9%-37.0%). Afterward, the volume of all scans increased further by 1.6% per week (95% CI: 1.3%-2.0%), where ultrasound increased the fastest by 2.4% (95% CI: 1.8%-2.9%). Mean in-person visits dropped by 47.4% when COVID-19 started (95% CI: 41.6%-52.6%) while virtual visits rose by 55.15-fold (95% CI: 4927%-6173%). In the pandemic (until September 26, 2020), in-person visits increased each week by 2.6% (95% CI: 2.0%-3.2%), but no change was observed for virtual visits (p -value = 0.10). CONCLUSIONS: Provision of diagnostic imaging and virtual visits at cancer diagnosis has been increasing since the start of COVID-19 and has exceeded prepandemic utilization levels. Future work should monitor the impact of these shifts on quality of delivered care.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".