Impact of the COVID-19 pandemic on esophageal cancer resource allocation: a systematic review
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic challenged global infrastructure. Healthcare systems were forced to reallocate resources toward the frontlines. In this systematic review, we analyze the impact of resource reallocation during the COVID-19 pandemic on the diagnosis, management, and outcomes of esophageal cancer (EC) patients. Methods: PubMed and Embase were systematically searched for articles investigating the impact of the COVID-19 pandemic on EC patients. Of the 1,722 manuscripts initially screened, 23 met the inclusion criteria. Results: Heterogeneity of data and outcomes reporting prohibited aggregate analysis. Reduced detection of EC and considerable variability in disease stage at presentation were noted during the COVID-19 pandemic. EC patients experienced delays in diagnostic and preoperative staging investigations but surgical resection was not associated with greater short-term morbidity or mortality. Modeling the impact of pandemic-related delays in EC care predicts significant reductions in survival with associated economic losses in the coming years. Conclusions: Amidst resource scarcity during the COVID-19 pandemic, the multidisciplinary management of patients with EC was affected at multiple stages in the care pathway. Although the complete ramifications of reductions in EC diagnosis and delays in care remain unclear, EC surgery was able to safely continue as a result of collaboration between centers, strict adherence to COVID-19 protective measures, and reallocation of healthcare resources towards the same. Ultimately, when healthcare systems are pushed to the brink, the downstream consequences of resource reallocation require judicious analysis to optimize overall patient outcomes.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".