Palliative care needs of stroke patients at a tertiary care center in South India
Bibliographic record
Abstract
Objectives: Stroke survivors have palliative care needs in multiple domains, which are overlooked. Accurate estimation of these is pivotal in ensuring proper rehabilitation and planning interventions to improve quality of life (QoL). We aimed to assess the palliative care needs of stroke patients in various domains in a structured manner at the neurology service of a tertiary care center in South India. Materials and Methods: Seventy-five consecutive stroke patients presenting to the neurology service were recruited over six months with assessment across various domains including symptom burden, physical domain, activities of daily living (ADL), psychiatric/psychological domain, and QoL at baseline and with follow-up at one month and three months. Results: Despite improvement in the conventional stroke impairment measures among stroke survivors, there were significant unmet needs across various domains; 98% were severely or entirely dependent on ADL at three-month follow-up; and pain and insomnia were the most frequent (33% incidence) troubling symptoms encountered. There were substantial mental health related issues. The QoL measurement tools employed were the stroke impact assessment questionnaire (SIAQ), a novel tool and the World Health Organization Quality Of Life Brief Version (WHO-QOL-BREF). SIAQ scores at one month showed that 19 patients (42.22%) had their QoL severely affected, and 36 patients (80%) showed the same trend at the three-month follow-up. WHO-BREF scores showed that 27 (62%) did not report good QoL, and 32 (73%) were found not to be satisfied with their health at a one-month follow-up. Conclusion: There is a significant burden of unmet palliative care needs among stroke survivors in India across various domains.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".