Ethical Considerations in Chemotherapy and Vaccines in Cancer Patients in Times of the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 situation is a worldwide health emergency with strong implications in clinical oncology. In this viewpoint, we address two crucial dilemmas from the ethical dimension: (1) Is it ethical to postpone or suspend cancer treatments which offer a statistically significant benefit in quality of life and survival in cancer patients during this time of pandemic?; (2) Should we vaccinate cancer patients against COVID-19 if scientific studies have not included this subgroup of patients? Regarding the first question, the best available evidence applied to the ethical principles of Beauchamp and Childress shows that treatments (such as chemotherapy) with clinical benefit are fair and beneficial. Indeed, the suspension or delay of such treatments should be considered malefic. Regarding the second question, applying the doctrine of double-effect, we show that the potential beneficial effect of vaccines in the population with cancer (or those one that has had cancer) is much higher than the potential adverse effects of these vaccines. In addition, there is no better and less harmful known solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".