Telehealth and Palliative Care for Patients With Cancer: Implications of the COVID-19 Pandemic
Bibliographic record
Abstract
It has been reported that the incidence of SARS-CoV-2 infection is higher in patients with cancer than in the general population and that patients with cancer are at an increased risk of developing severe life-threatening complications from COVID-19. Increased transmission and poor outcomes noted in emerging data on patients with cancer and COVID-19 call for aggressive isolation and minimization of nosocomial exposure. Palliative care and oncology providers are posed with unique challenges due to the ongoing COVID-19 pandemic. Telepalliative care is the use of telehealth services for remotely delivering palliative care to patients through videoconferencing, telephonic communication, or remote symptom monitoring. It offers great promise in addressing the palliative and supportive care needs of patients with advanced cancer during the ongoing pandemic. We discuss the case of a 75-year-old woman who was initiated on second-line chemotherapy, to highlight how innovations in technology and telehealth-based interventions can be used to address patients' palliative and supportive care needs in the ongoing epidemic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".