Dialysis Decision Making and Preferences for End-of-Life Care: Perspectives of Pakistani Patients Receiving Maintenance Dialysis
Bibliographic record
Abstract
CONTEXT: Previous studies from the U.S. and Canada report deficiencies in informed decision making and a need to improve end-of-life (EoL) care in patients undergoing dialysis. However, there is a paucity of literature on these issues in Pakistani dialysis patients, who differ from Western patients in culture, religion, and available health care services. OBJECTIVES: To study informed dialysis decision-making and EoL attitudes and beliefs in Pakistani patients receiving dialysis. METHODS: We used convenience sampling to collect 522 surveys (90% response rate) from patients in seven different dialysis units in Pakistan. We used an existing dialysis survey tool, translated into Urdu, and backtranslated to English. A facilitator distributed the survey, explained questions, and orally administered it to patients unable to read. RESULTS: Less than one-fourth of the respondents (23%) felt informed about their medical condition, and 45% were hopeful that their condition would improve in the future. More than half (54%) wished to know their prognosis, and 80% reported having no prognostic discussion. Almost 63% deemed EoL planning important, but only 5% recalled discussing EoL decisions with a doctor during the last 12 months. Nearly 62% of the patients regretted their decision to start dialysis. Patients' self-reported knowledge of hospice (5%) and palliative care (7.9%) services was very limited, yet 46% preferred a treatment plan focused on comfort and symptom management rather than life extension. CONCLUSION: Pakistani patients reported a need for better informed dialysis decision making and EoL care and better access to palliative care services. These findings underscore the need for palliative care training of Pakistani physicians and in other developing countries to help address communication and EoL needs of their dialysis 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".