COVID-19 and supportive cancer care: key issues and opportunities
Bibliographic record
Abstract
PURPOSE OF REVIEW: The disruption to people's lives, including financial impacts, morbidity and loss of life caused by the Coronavirus disease (COVID-19) pandemic requires a dramatic transformation of cancer care delivery, including supportive care. This paper focuses on issues of supportive care in the context of the pandemic, and the extent to which these issues will impact supportive cancer care post-COVID-19. RECENT FINDINGS: Cancer care, including supportive care delivery, has had to be dramatically altered during the COVID-19 pandemic, including reallocation of human resources, repurposing of existing physical space, amplified use of telehealth and other remote patient monitoring technologies, changes to treatment and follow-up care patient schedules, among others. These changes have resulted in psychosocial sequelae for cancer patients (including anxiety, stress, loss of control), financial toxicity, and risk of disengagement from treatment and follow-up care. SUMMARY: COVID-19 has seriously disrupted cancer treatment and supportive care for patients and survivors. This paper highlights implications for clinical practice during and post-COVID-19, including the durability of practice adaptations and opportunities for research into mechanisms to support supportive care post the pandemic, including the advancement of eHealth technologies and alternative models of care that integrate community resources, primary care and allied health disciplines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".