Nondisclosure and the Impact on Symptom Burden in Muslims With Advanced Cancer: A Review of Five Symptom Assessment Tools
Bibliographic record
Abstract
Cultural and religious beliefs in certain regions, including many Muslim-majority countries, often lead families to request the withholding of medical information from patients. Despite the availability of validated symptom assessment tools for chronic conditions such as cancer, chronic obstructive pulmonary disease, neurological disorders, heart disease, and renal failure, the effect of nondisclosure on symptom burden remains insufficiently explored. This scoping review examines the role of symptom assessment tools in evaluating the outcomes of nondisclosure in advanced cancer patients, particularly in the United Arab Emirates (UAE). The review assesses five key instruments: the Edmonton Symptom Assessment Scale (ESAS), its revised version (ESAS-r), the Memorial Symptom Assessment Scale (MSAS), the short form (MSAS-SF), and the Palliative Care Outcome Scale (POS). An analysis of the psychometric properties, strengths, and limitations of these tools highlights their utility in understanding symptom burden and psychological well-being. The findings suggest that comprehensive tools like the MSAS provide valuable assessments but require further validation to confirm their effectiveness across diverse clinical and cultural settings. Future research should prioritize adapting these tools for wider application and ensuring their reliability and validity in measuring the impact of nondisclosure on symptom burden across various diagnoses, including both cancer and noncancer conditions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".