Impact of the COVID-19 Pandemic on Cancer Care: A Global Collaborative Study
Bibliographic record
Abstract
PURPOSE: The COVID-19 pandemic affected health care systems globally and resulted in the interruption of usual care in many health care facilities, exposing vulnerable patients with cancer to significant risks. Our study aimed to evaluate the impact of this pandemic on cancer care worldwide. METHODS: We conducted a cross-sectional study using a validated web-based questionnaire of 51 items. The questionnaire obtained information on the capacity and services offered at these centers, magnitude of disruption of care, reasons for disruption, challenges faced, interventions implemented, and the estimation of patient harm during the pandemic. RESULTS: A total of 356 centers from 54 countries across six continents participated between April 21 and May 8, 2020. These centers serve 716,979 new patients with cancer a year. Most of them (88.2%) reported facing challenges in delivering care during the pandemic. Although 55.34% reduced services as part of a preemptive strategy, other common reasons included an overwhelmed system (19.94%), lack of personal protective equipment (19.10%), staff shortage (17.98%), and restricted access to medications (9.83%). Missing at least one cycle of therapy by > 10% of patients was reported in 46.31% of the centers. Participants reported patient exposure to harm from interruption of cancer-specific care (36.52%) and noncancer-related care (39.04%), with some centers estimating that up to 80% of their patients were exposed to harm. CONCLUSION: The detrimental impact of the COVID-19 pandemic on cancer care is widespread, with varying magnitude among centers worldwide. Additional research to assess this impact at the patient level is required.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".