The longitudinal impact of COVID-19 on the diagnosis and treatment of lung cancer at a Canadian academic center: Interim analysis from a retrospective chart review.
Bibliographic record
Abstract
e18737 Background: The coronavirus disease 2019 (COVID-19) has significantly impacted clinical activities across many medical specialties. The care of lung cancer (LC) patients is no exception. Our group recently reported that the rate of new lung cancer diagnoses declined by 35% during the first year of COVID-19 pandemic (year 2020). The objective of the present study is to continue to evaluate changes in lung cancer diagnosis and management during 3rd and 4th waves of the pandemic (2021). Methods: This is a retrospective chart review study including patients diagnosed with lung cancer between March 1st 2019 and February 1st 2022 at the Jewish General Hospital, Montreal, QC, Canada. We compared 3 cohorts: Cohort 1: 2019 (pre-COVID). Cohort 2: 2020 (1st year of COVID). Cohort 3: 2021 (2nd year of COVID; reporting for 10 months of 2021). Results: A total of 388 patients were diagnosed with lung cancer throughout the three-year study: 130 in cohort 1, 103 in cohort 2 and 155 in cohort 3. Although there was a 35% decline of new lung cancer (LC) diagnoses observed in the 1st year of COVID, there was a 50% increase in diagnoses during the 2nd year comparing to the first year and a 19.2% increase compared to the pre-COVID year. Stage 4 LC diagnoses increased by 29% in 2021 compared to 2019 (88 cases vs 68 cases) and by 54% compared to 2020 (88 cases vs 57 cases). Early stage curative treatments (which had significantly changed in 2020, as the use of radiation therapy [RT] increased by 125% and surgery decreased by 38%), approached pre-pandemic levels in 2021. In 2021 we observed a decrease in RT and a significant increase in surgical resections. Analyses of wait time for initiation of treatment is still ongoing. Conclusions: The present study represents interim data in our ongoing effort to evaluate the dynamic impacts of the COVID-19 pandemic on diagnosis and delivery of LC care. The pattern of lung cancer treatment modalities for early stage disease appears to be recovering to pre-COVID rates. However, the significant decrease in lung cancer diagnoses during the first year of the pandemic has resulted in an increase in new diagnoses during the 2nd year at unfortunately more advanced stage of disease.[Table: see text]
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".