Medicina paliativa hospitalaria: modelo de atención implementado en un hospital universitario
Bibliographic record
Abstract
BACKGROUND: Palliative Medicine (PM) is a specialty whose objective is to prevent and alleviate suffering associated with advanced diseases. Hospital palliative medicine has benefits in symptom control, quality of life and cost containment. Hospital PM support teams that serve as referral specialists are in charge of a PM care model. AIM: To describe the clinical experience of a PM support team in a tertiary hospital in Chile. MATERIAL AND METHODS: Review of clinical records of patients referred to a hospital PM support team between March 2015 and July 2018. Administrative data of referrals, sociodemographic and clinical characteristics of patients, their investigated problems and the interventions proposed by the PM team were described. RESULTS: During the study period, 790 referrals were registered, most of them from the internal medicine department (31%) or critical care (24%). During the study period, the number of annual referrals increased from 177 to 237 and the time lapse after hospital admission decreased from five to three days. The mean age of patients was 65.8 years and their main diagnosis was an oncological disease in 81%. The most frequently identified symptoms were fatigue in 71% of patients, depression in 68% and pain in 60%. The main interventions proposed by the PM team were communication support in 64% of patients, analgesia in 62% and education for family caregivers in 49%. CONCLUSIONS: The hospital PM team proposes a care model that allows the evaluation and a therapeutic approach for patients suffering from advanced diseases, using a multidimensional perspective including their families.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".