Impact of COVID-19 Pandemic on Non-Small Cell Lung Cancer Care
Bibliographic record
Abstract
We assessed the impact of COVID-19 on healthcare visits, timing of stage IV NSCLC diagnosis and immunotherapy initiation, and rates of switching to extended dosing schedules of immunotherapies among patients with stage IV NSCLC. This retrospective study examined electronic health record data of adult patients receiving treatment for stage IV NSCLC within The US Oncology Network and Onmark. Endpoints were compared for February-July 2019 (before COVID) vs. February-July 2020 (during COVID). The study found rapid decreases in numbers of patients with clinic/vital visits, immunotherapy initiations, and new diagnoses of stage IV NSCLC during April-May 2020 vs. April-May 2019. The rate of delays of immunotherapy administrations and proportions of patients with such delays increased from February to March of 2020. These patterns may have resulted from the increase in COVID-19 cases during this period and the corresponding quarantine and lockdowns. However, when comparing pre COVID-19 and during COVID-19 for May and after, the differences in delay of immuno-oncology administrations became less marked, likely due to lifting of lockdowns. The rate of switching from shorter to longer dosing schedules increased from May-July 2020. This was mainly attributed to pembrolizumab, likely due to FDA approval of the pembrolizumab 6W dosing schedule in April 2020.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".