Imaging and physician visits at cancer diagnosis: COVID-19 pandemic impact on cancer care.
Bibliographic record
Abstract
1522 Background: Understanding how cancer system responded to the first wave of the COVID-19 pandemic has crucial implications to de-escalation measures in future waves. Here we examined the pandemic impact on the provision of diagnostic imaging (MRI, CT, and ultrasound) and physician visits (virtual and in-person) at cancer diagnosis in Ontario, Canada. Methods: For each week of June 26, 2016–September 26, 2020, we identified cancer diagnoses whose time around diagnosis (91 days +/- the date of diagnosis) fell into this week and restricted those diagnoses to be one per person-day and to patients aged 18+ at the beginning of that week. For these cancer patients, we used physician claims database to identify diagnostic imaging and visits received around cancer diagnosis. In separate segmented negative binomial regression procedures, we assessed the trends in weekly volume of these services per thousand cancer patients in pre-pandemic (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 in the pandemic (March 15, 2020 to September 26, 2020). Results: Among 403,561 cancer patients in the cohort, 41,476 (10.3%) were diagnosed in the pandemic. As COVID-19 arrived, mean diagnostic imaging 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%) and MRI did not change (p-value = 0.27). Afterwards, 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% for each week (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 5515% (95% CI: 4927%-6173%). In the pandemic era, 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 already exceeded pre-pandemic utilization levels. These findings imply the feasibility of combining virtual consultations with diagnostic imaging to manage new cancer patients and highlight the need to monitor the quality of these services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".