Evaluating the Impact of the COVID-19 Pandemic on New Cancer Diagnoses and Oncology Care in Manitoba
Bibliographic record
Abstract
Individuals with cancer are vulnerable to infection with SARS-CoV-2, the virus causing COVID-19. Physical distancing, the reallocation of health care resources, and the implementation of procedures to reduce the spread of COVID-19 may also have serious consequences for people with cancer. We evaluated the impact of COVID-19 on new cancer diagnoses and oncology care in Manitoba, Canada using an interrupted time series design and data from the Manitoba Cancer Registry and CancerCare Manitoba's (CCMB) electronic medical record. In April 2020, there was a 23% decrease in new cancer diagnoses, a 21% decrease in pathology reports, and a 43% reduction in surgical resections. There was no difference in new cancer diagnoses by August 2020, surgery by July 2020, and pathology reports by September 2020. From April 2020 to June 2021, there was a 13% decrease in radiotherapy (RT) fractions, an 18% decrease in UCC visits, and a 52% decrease in in-person visits. There was no change in intravenous chemotherapy visits per month, first RT visits, or overall patient visits. The impact of COVID-19 on shifts in the stage at diagnosis and survival will be assessed in future analyses.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".