The Impact of COVID-19 on the Diagnosis and Treatment of Lung Cancer at a Canadian Academic Center: A Retrospective Chart Review
Bibliographic record
Abstract
The large burden of COVID-19 on health care systems worldwide has raised concerns among medical oncologists about the impact of COVID-19 on the diagnosis and treatment of lung cancer patients. In this retrospective cohort study, we investigated the impact of COVID-19 on lung cancer diagnosis and treatment before and during the COVID-19 era. New lung cancer diagnoses decreased by 34.7% during the pandemic with slightly more advanced stages of disease, there was a significant increase in the utilization of radiosurgery as the first definitive treatment, and a decrease in both systemic treatment as well as surgery compared to the pre-COVID-19 era. There was no significant delay in starting chemotherapy and radiation treatment during the pandemic compared to pre-COVID-19 time. However, we observed a delay to lung cancer surgery during the pandemic time. COVID-19 seems to have had a major impact at our lung cancer center on the diagnoses and treatment patterns of lung cancer patients. Many oncologists fear that they will see an increase in newly diagnosed lung cancer patients in the coming year. This study is still ongoing and further data will be collected and analyzed to better understand the total impact of the COVID-19 pandemic on our lung cancer patient population.
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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 | 0.000 | 0.001 |
| 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".