Palliative care consultations for non-malignant diseases: Experience from a public hospital in Western India
Bibliographic record
Abstract
Background: Palliative care (PC) consultation has been shown to improve patient's quality of life (QoL), reduce symptom burden, decrease hospital stay, and provide holistic care for patients with life-limiting diseases. Aim: To asses impact of palliative medicine consultation in symptom management in patients with non-oncological medical conditions in a tertiary care hospital. Design: A record based study with study period of six months, from August 2021 to January 2022 was conducted. Patients with complete record were included for analysis. Setting/Participants: Included patients above 18 years of age with a non-oncological medical conditions referred for a newly established palliative medicine clinic in a tertiary care hospital. Results: A total of 83 patient records were analyzed, of which 30 (36.1%) were female and 53 (63.9%) were male. Among patients referred for Palliative Medicine consultation, 38 (45.8%) had neurological disease, followed by chronic renal diseases in 31 (37.3%) patients. There was a statistically significant improvement in the Edmonton Symptom Assessment System (ESAS) score on the follow-up visit. Discussion regarding Do Not Resuscitate (DNR) status was conducted in 20% of patients, and all patients or their families consented to the directive. Home-based Palliative Care arrangements were provided for 10% of patients. Conclusion: Palliative Medicine consultation brought about a significant reduction in anxiety and fatigue among patients with non-oncological medical conditions. It also helped in reducing the severity of symptoms in these patients.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".