The Impact of COVID-19 on the Diagnosis and Treatment of Lung Cancer over a 2-Year Period at a Canadian Academic Center
Bibliographic record
Abstract
BACKGROUND: We have recently reported a 35% drop in new lung cancer diagnoses and a 64% drop in lung cancer surgeries during the first year of the pandemic. METHODS: The target population was divided into three cohorts: pre-COVID-19 (2019), first year of COVID-19 (2020), and second year of COVID-19 (2021). RESULTS: The number of new lung cancer diagnoses during the second year of the pandemic increased by 75%, with more than 50% being in the advanced/metastatic stage. There was a significant increase in cases with multiple extrathoracic sites of metastases during the pandemic. During the first year of the pandemic, significantly more patients were treated with radiosurgery compared to the pre-COVID-19 year. During the second year, the number of radiosurgery and surgical cases returned to pre-COVID-19 levels. No significant changes were observed in systemic chemotherapy and targeted therapy. No statistical difference was identified in the mean wait time for diagnosis and treatment during the three years of observation. However, the wait time for surgery was prolonged compared to the pre-COVID-19 cohort. CONCLUSIONS: The significant drop in new diagnoses of lung cancer during the first year of the pandemic was followed by an almost two-fold increase in the second year, with the increased rate of metastatic disease with multiple extra-thoracic site metastases. Limited access to surgery resulted in the more frequent use of radiosurgery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".