Palliative Care Support Improves Quality of Life of Patients with Advanced Interstitial Lung Disease
Bibliographic record
Abstract
Abstract BackgroundFibrotic interstitial lung disease associates with a considerable symptom burden, deterioration of quality of life, and mortality; thus, Palliative Care is an alternative for symptoms control. The aim was to analyze its effect in the quality of life.MethodsObservational, prospective study that included patients referred to Palliative Care. Demographic data, baseline functional capacity, symptoms intensity, and quality of life were registered through unidimensional and multidimensional scales before the intervention and then at 30- and 90- days ups. The primary outcome was to analyze the score change through an analysis of variance.ResultsFourteen patients were included with a median age of 63 years (Interquartile Range 54–71), 85% were women. Baseline evaluation confirmed a moderate functional impairment (Karnofsky 65, Interquartile Range 50–70) and deterioration of quality of life (Saint George´s Respiratory Questionnaire-I, 91 points, Interquartile Range 84–96). Frequent baseline symptoms documented by the Edmonton Symptom Assessment System were cough, dyspnea, tiredness, anxiety/depression, and pain. After 90 days of Palliative Care, a significant improvement was observed both in symptoms control and quality of life, based on a 20-point decrease in Saint George´s Respiratory Questionnaire-I (p = 0.008).ConclusionsThe Palliative Care intervention improves symptoms control, and the quality of life patients perceive.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".