Time-to-first treatment for new cancer diagnoses before and during the COVID-19 pandemic in Manitoba, Canada.
Bibliographic record
Abstract
e23103 Background: Disruptions to health care during the COVID-19 pandemic raises the possibility of delays in treatment for individuals diagnosed with cancer. It is critical that we evaluate the association between the COVID-19 pandemic and time-to-first treatment (TTFT) to address public and patient anxiety, inform recovery efforts, and identify strategies to reduce the cancer system’s vulnerability to future disruptions. Objective: To examine the association between the COVID-19 pandemic and time from cancer diagnosis to first treatment in Manitoba, Canada. Methods: A retrospective, population-based, quasi-experimental study that included individuals diagnosed with breast, colon, rectal, hematologic, lung, or prostate cancer between January 2015 and December 2021 was performed. Interrupted time series analyses with competing risk models were used to compare TTFT for those diagnosed before and after the start of the pandemic. Time-to-first treatment was calculated from diagnosis date to first treatment date (i.e., chemotherapy, hormone therapy, immunotherapy, radiotherapy, or surgery). Death without treatment was included as a competing risk. Follow-up time was censored at three months post-diagnosis. Sub-hazard ratios (SHR) and 95% confidence intervals (CI) were reported. Higher SHR indicates earlier treatment. The delta restricted mean time-to-treatment (D_RMTT) at three months was calculated to complement SHR values, where negative times indicate earlier treatment. Since observation is standard of care for low-risk prostate cancer and androgen deprivation therapy was widely used to temporize intermediate and high-risk prostate cancer patients, analyses were restricted to stage IV prostate cancer. Results: Time-to-first treatment was shorter for individuals diagnosed with breast cancer during the COVID-19 pandemic period (SHR: 1.40; 95% CI 1.24-1.57; D_RMTT: -6.6 days). Time-to-first treatment was also shorter for individuals diagnosed with colon cancer from April 2020 to June 2021 (SHR: 1.25; 95% CI 1.09-1.42; D_RMTT: -6.5 days) but not from July 2021 to December 2021 (SHR: 0.96; 95% CI 0.81-1.15; D_RMTT: 1.2 days). All other analyses did not demonstrate a statistically significant change in TTFT: rectal cancer (SHR: 1.00; 95% CI: 0.83-1.21; D_RMTT: 0.0 days), hematological cancers (SHR: 0.87; 95% CI: 0.74-1.01; D_RMTT:3.9 days); lung cancer (SHR: 1.09; 95% CI: 0.97-1.23; D_RMTT: -2.3 days); and stage IV prostate cancer (SHR: 1.15; 95% CI: 0.89-1.49; D_RMTT: -4.1 days). Conclusions: The COVID-19 pandemic has not increased the time from diagnosis date to first treatment date for individuals diagnosed with breast, colon, rectal, hematologic, lung, or stage IV prostate cancer in Manitoba, Canada.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".