The impact of COVID-19 on the survival outcome of lung cancer at a Canadian academic centre: A real-world data analysis.
Bibliographic record
Abstract
e21200 Background: The Covid-19 pandemic directly affected the screening, diagnosis, and treatment of lung cancer patients. Our group recently reported that the rate of new lung cancer diagnoses declined during the first year and significantly increased during the second of COVID-19 pandemic. The effect of COVID-19 on lung cancer treatment outcomes in literature is still limited and mostly reported either as predictive survival using prioritization and modeling techniques. In this study, we aimed to quantify the effect of COVID-19 on lung cancer survival using real world data collected at the Jewish General Hospital, Montreal. Methods: This is a retrospective chart review study including patients diagnosed with lung cancer between March 2019 and March 2022. We compared 3 cohorts: Cohort1: 2019 (pre-COVID) Cohort2: 2020 (1st year of COVID) Cohort3: 2021 (2nd year of COVID). Results: A total of 417 patients were diagnosed and treated with lung cancer at our centre throughout the three-year study: 130 in 2019, 103 in 2020 and 184 in 2021. Although the proportion of advanced/metastatic stage lung cancer remained the same for the three cohorts, there was a significant increase in the late-stage presentation during the pandemic. The proportion of M1c (multiple extrathoracic sites) cases in Cohorts 2 and 3 was 57% and 51% respectively compared to 31% in cohort 1 (p < 0.05). Median survival for early and locally advanced stages of lung cancer was similar in the 3 cohorts. However, the subgroup of patients diagnosed in the advanced/metastatic stage had a significantly increased risk of death during the pandemic (cohorts 2 and 3). The 6-month mortality rate was 53% in 2021 compared to 47% in 2020 and 29% in 2019 (p = 0.004). The median survival in this subgroup of patients decreased significantly from 13 months in 2019 to 6 months in 2020 and 5 months in 2021 (Table 1). Conclusions: The present study represents the impacts of the COVID-19 on lung cancer survival outcome. The COVID-19 pandemic potentially caused the significant shift to M1c stage and contributed to an increase 6 month mortality rate and poorer overall survival in the advanced/metastatic lung cancer patients diagnosed and treated during the pandemic.[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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".