P-2073. COVID-19 Vaccine Effectiveness in Patients with Lung Cancer and Mesothelioma in the Omicron Era
Bibliographic record
Abstract
Abstract Background Since the Omicron variant emerged, uptake of booster doses of COVID-19 vaccines has gradually decreased both in the general population and among individuals at increased risk of severe COVID-19, such as patients with lung cancer. COVID-19 vaccine effectiveness (VE) has not been estimated in patients with lung cancer in the Omicron era. Methods In this test-negative design study using linked population-based cancer registry, health administrative, vaccination, and public health surveillance databases, we included all patients with active lung cancer or mesothelioma living in Ontario, Canada, who were tested for SARS-CoV-2 by RT-PCR from January 2, 2022, to August 31, 2023. We estimated VE against COVID-19-related severe outcomes (hospitalization or death) 7-179 days and ≥180 days following vaccination. Results During the study period, 13,622 patients with active lung cancer or mesothelioma underwent SARS-CoV-2 testing, with 1,371 (10.1%) positive. After exclusions, we analyzed 6,037 testing episodes, including 1,354 test-positive and 4,683 randomly selected test-negative episodes. Overall, COVID-19-associated hospitalization and mortality rates were 7.3% and 4.0%, respectively. Across both groups, 126/439 hospitalized patients (28.7%) and 116/5,598 non-hospitalized patients died (2.1%) (p=0.001). After multivariable adjustment, VE against severe COVID-19 was 56% (95%CI, 29%, 72%) 7-179 after vaccination and 10% (-45%, 44%) ≥180 days after vaccination. Conclusion Overall, COVID-19 VE against severe outcomes appears to be considerably lower for patients with active lung cancer or mesothelioma than the general population and decreases substantially beyond 180 days after vaccination. Our finding supports administration of COVID-19 booster doses in patients with lung cancer and mesothelioma every 6 months. Disclosures All Authors: No reported disclosures
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".