Survival of cancer patients under treatment with the palliative care team in a Brazilian hospital in São Paulo
Bibliographic record
Abstract
Introduction: The Karnofsky Performance Status Scale is a relevant functional evaluation instrument that can be used to determine which patients should be followed by multidisciplinary palliative care teams. Objective: To analyze the clinical outcomes of patients with performance status lower than 70%, according to the Karnofsky Scale, who received care from a palliative care team compared to those who did not receive care from a palliative care team. Methods: In this retrospective cohort, follow-up of cancer patients by the palliative care team for 10 days was considered the exposure factor, while the dependent variable was patient survival. Data were extracted from medical records and descriptive and survival curve analyses were conducted. Results: Among 581 participants in the sample, 42.5% had metastasis, and the most prevalent medical diagnosis was gastrointestinal cancer (29.1%). Fifty-one (8.7%) were followed by the palliative care team. The mortality rate during the 10 days in the sample was 10.8%, and the rate was higher (15.7%) among patients followed by the palliative care team. Conclusion: Patients with a performance status below 70% who were followed by the palliative care team had poorer clinical conditions and a shorter survival than those who were not followed up by the team.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".