Impact of the COVID-19 Pandemic on Diagnosis and Multidisciplinary Treatment of NSCLC in Ontario, Canada
Bibliographic record
Abstract
Introduction: The coronavirus disease 2019 pandemic disrupted cancer care delivery globally, with many jurisdictions reporting reductions in lung cancer diagnoses and delays in treatment. In Ontario, Canada, both institutional and provincial data have reported mixed trends in NSCLC presentation and care. This study aimed to assess the short-term impact of the coronavirus disease 2019 pandemic on NSCLC diagnoses and treatment pathways across Ontario using population-level data from Cancer Care Ontario administrative health databases. Methods: We conducted a retrospective cohort study of patients diagnosed with NSCLC in Ontario between January 1, 2019 and December 31, 2020. The cohort was created using relevant diagnostic codes and linked provincial databases to evaluate diagnostic trends and access to surgical, medical, and radiation oncology services. Statistical analyses included Poisson regression to assess changes in diagnosis rates and multivariable linear regressions to evaluate wait times, adjusting for age, sex, income quintile, and geographic region. Results: A total of 13,407 NSCLC cases were identified. There was a 6% overall decline in diagnoses in 2020, with a 31% drop during quarter 2 (April-June 2020). The mean wait times for surgical consultation and treatment and also medical and radiation oncology consults improved or remained stable. No delays were found in systemic therapy initiation. Multivariable analyses confirmed these findings. Conclusions: NSCLC care delivery in Ontario remained stable during the early pandemic period. Declines in diagnosis warrant further investigation using longer-term data. Real-time data systems are essential for future pandemic preparedness and response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".