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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".