Cancer care during COVID-19: Data from 157 patient organizations.
Bibliographic record
Abstract
e18564 Background: Representatives from 8 global cancer coalitions/alliances, representing 650 cancer patient groups and the interests of over 14 million patients have come together during the pandemic to review and evaluate the patient-perspective impact. Cancer services have faced challenges as a result of COVID-19, including suspension of screening and diagnostic services; delays in diagnosis leading to higher mortality rates; cancellation/deferral of life-saving treatments; changes in treatment regimens and suspension of vital research. For organisations that provide support to cancer patients, declining income, the need to reduce staff and move to virtual working practices has put extra strain while demand for support due to the pandemic has increased. Methods: 5 coalitions surveyed their member organisations. A number of coalitions consulted their members by individual surveys or consultations. Results: A survey of 157 organisations representing advanced breast, bladder, lymphoma, ovarian and pancreatic cancer patient groups from 56 countries found that 57% experienced an average increase of 44% in patient calls and emails. 45% reported that their future viability may be under threat because of the impact of COVID-19 on income. Examples of good practice were reported where healthcare systems have acted to protect patients and cancer services. These include the introduction of COVID-free centres, separation of cancer patients from those who may have COVID-19, and the introduction of virtual and telemedicine services. Organisations have also introduced new ways of working including virtual psychological support services and app-based support groups. These best practices should form part of a global plan of action for future health crisis. Conclusions: Collaboration between patient advocacy organisations, governments and health services is needed to ensure the ground lost to the COVID-19 pandemic is regained. Action is required to restore cancer services safely and effectively without delay. Additional resources for organisations that support cancer patients are required to ensure that they continue to provide vital services. Finally, a global plan of action for cancer is required to meet the challenges of any future health crisis.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".