Measuring the impact of COVID-19 on cancer survival using an interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated the impact of the COVID-19 pandemic on cancer survival. Those studies that have included pandemic vs prepandemic comparisons can mask differences during different periods of the pandemic such as COVID-19 waves. The objective of this study was to investigate the impact of the COVID-19 pandemic on cancer survival using an interrupted time series analysis and to identify time points during the pandemic when observed survival deviated from expected survival. METHODS: A retrospective population-based cohort study that included individuals diagnosed with cancer between January 2015 and September 2021 from Manitoba, Canada, was performed. Interrupted time series analyses with Royston-Parmar models as well as Kaplan-Meier survival estimates and delta restricted mean survival times at 1 year were used to compare survival rates for those diagnosed before and after the pandemic. Analyses were performed for 11 cancer types. RESULTS: Survival at 1 year for most cancer types was not statistically different during the pandemic compared with prepandemic except for individuals aged 50-74 years who were diagnosed with lung cancer from April to June 2021 (delta restricted mean survival times = -31.6 days, 95% confidence interval [CI] = -58.3 to -7.2 days). CONCLUSIONS: With the exception of individuals diagnosed with lung cancer, the COVID-19 pandemic did not impact overall 1-year survival in Manitoba. Additional research is needed to examine the impact of the pandemic on long-term cancer survival.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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 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".