Resource allocation during the coronavirus disease 2019 pandemic and the impact on patients with lung cancer: a systematic review
Bibliographic record
Abstract
OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic resulted in unprecedented tolls on both economies and human life. Healthcare resources needed to be reallocated away from the care of patients and towards supporting the pandemic response. In this systematic review, we explore the impact of resource allocation during the COVID-19 pandemic on the screening, diagnosis, management and outcomes of patients with lung cancer during the pandemic. METHODS: PubMed and Embase were systematically searched for articles investigating the impact of the COVID-19 pandemic on patients with lung cancer. Of the 1605 manuscripts originally screened, 47 studies met the inclusion criteria. RESULTS: Patients with lung cancer during the pandemic experienced reduced rates of screening, diagnostic testing and interventions but did not experience worse outcomes. Population-based modelling studies predict significant increases in mortality for patients with lung cancer in the years to come. CONCLUSIONS: Reduced access to resources during the pandemic resulted in reduced rates of screening, diagnosis and treatment for patients with lung cancer. While significant differences in outcomes were not identified in the short term, ultimately the effects of the pandemic and reductions in cancer screening will likely be better delineated in the coming years. Future consideration of the long-term implications of resource allocation away from patients with lung cancer with an attempt to provide equitable access to healthcare and limited interruptions of patient care may help to provide the best care for all patients during times of limited resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".