Specialized Palliative Care and the Quality of Life for Hospitalized Cancer Patients at a Low-Resource Hospital in India.
Bibliographic record
Abstract
AIM: This study aimed to compare the quality of life (QoL) of cancer patients, with an Eastern Cooperative Oncology Group (ECOG) performance of 3-4, in contact with or without contact, with a specialized palliative care unit (PCU) at a low-resource governmental cancer hospital, as well as studying the impact of this contact on the QoL in their caregivers. MATERIALS AND METHODS: Hospitalized patients with an ECOG performance of 3 or 4 and their primary caregiver were asked to participate in this observational study. Patients in contact with the specialized PCU and their closest caregivers formed Group A, while patients and families without this contact formed Group B. Contact was mainly one consultation. The patients were asked to complete the Palliative Care Outcome Scale (POS), and the caregivers were asked to complete the Hospital Anxiety and Depression Scale (HADS) and the distress thermometer (DT). RESULTS: There was no statistically significant difference between the median POS values of the patient groups, neither regarding the total sum nor per any item. There were also no statistically significant differences between the median HADS values and median DT values when comparing the caregivers to Group A and B. CONCLUSION: Consultation with a specialized PCU at this tertiary referral center did not alter the QoL of patients with an ECOG performance of 3-4 nor did it affect the psychological well-being of their caregivers. We argue that monitoring prescribed treatment and follow-up is a necessary component of PC.
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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 | 0.000 | 0.000 |
| 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".