Identification and characteristics of patients with palliative care needs in Brazilian primary care
Bibliographic record
Abstract
BACKGROUND: The Brazilian healthcare system offers universal coverage but lacks information about how patients with PC needs are serviced by its primary care program, Estratégia Saúde da Família (ESF). METHODS: Cross-sectional study in community settings. Patients in ESF program were screened using a Palliative Care Screening Tool (PCST). Included patients were assessed with Karnofsky Performance Scale (KPS), Edmonton Symptom Assessment System (ESAS) and Palliative Care Outcome Scale (POS). RESULTS: Patients with PC needs are accessing the ESF program regardless of there being no specific PC support provided. From 238 patients identified, 73 (43 women, 30 men) were identified as having a need for PC, and the mean age was 77.18 (95 % Confidence Interval = ±2,78) years, with non-malignant neurologic conditions, such as dementia and cerebrovascular diseases, being the most common (53 % of all patients). Chronic conditions (2 or more years) were found in 70 % of these patients, with 71 % scoring 50 or less points in the KPS. Overall symptom intensity was low, with the exception of some cases with moderate and high score, and POS average score was 14.16 points (minimum = 4; maximum = 28). Most patients received medication and professional support through the primary care units, but limitations of services were identified, including lack of home visits and limited multi-professional approaches. CONCLUSION: Patients with PC needs were identified in ESF program. Basic health care support is provided but there is a lack of attention to some specific needs. PC policies and professional training should be implemented to improve this area.
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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 | 0.003 |
| 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 | 0.001 | 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".