Care for patients with advanced cancer in the last weeks of life in Brazil
Bibliographic record
Abstract
ABSTRACT Despite all advances in the treatment of neoplasms and substantial increases in fiveyear survival rates, most patients still die due to their diseases. Late diagnosis in some circumstances and resistance mechanisms throughout treatment still cause most patients to require palliative care integrated with cancer treatment, since diagnosis. Most palliative care interventions can and should be carried out by the oncologist, with reference to the multidisciplinary team specialized in palliative care in the most critical moments of clinical evolution. It is important that the oncologist develops their skills in this scenario and knows how to recognize the moment of referral. The following text outlines the basic skills that are expected of oncologists, such as recognition of the prognosis, identification and correct assessment of symptoms, definition of the time to stop antineoplastic therapy, how to communicate these aspects to patients and family, how to involve psychosocial and spiritual issues and, finally, how to stay within the limits established by modern bioethics. This work consists of brief recommendations for oncologists working in Brazil.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".