First Comprehensive Assessment of Quality of Life in Cancer Patients in Afghanistan: Insights From Herat Regional Hospital
Bibliographic record
Abstract
BACKGROUND: This study marks the first extensive evaluation of quality of life (QoL) among cancer patients in Afghanistan, carried out at the Oncology Department of Herat Regional Hospital. Its primary objective was to assess the QoL of cancer patients and to determine key factors influencing it, utilizing the validated European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 instrument. AIMS: The study aimed to determine overall QoL levels among Afghan cancer patients and to examine the impact of sociodemographic and clinical characteristics, including disease stage and gender differences, on functional and symptom related domains. METHODS AND RESULTS: A total of 230 patients diagnosed with cancer participated in this cross-sectional survey conducted between February and May 2024. Data regarding sociodemographic characteristics and clinical profiles were collected, and QoL outcomes were examined across both functional and symptom-related domains. The findings indicated that esophageal and gastric cancers were the most common types observed, with the majority of patients presenting at stage two of the disease. Among the functional domains, cognitive functioning received the highest scores, whereas social functioning scored the lowest. Financial hardship emerged as the most burdensome symptom reported. Disease progression, marked by advancing cancer stages, was associated with significant declines in physical and role functioning, along with increases in symptoms such as dyspnea and insomnia. Gender differences were notable, with male patients reporting higher overall QoL compared to female patients. Economic challenges were found to have a considerable negative effect on QoL outcomes. CONCLUSION: As a pioneering study in Afghanistan, these results emphasize the pressing need for interventions that address the physical, psychological, and economic hardships faced by cancer patients. The study further stresses the importance of promoting early diagnosis, developing individualized treatment plans, and providing comprehensive supportive care to enhance patient well-being. These insights offer a foundation for developing cancer care policies and integrating standardized QoL assessment tools within clinical practice in Afghanistan and similar resource-limited environments. These findings provide crucial evidence for policymakers to develop targeted, gender-sensitive interventions to mitigate financial toxicity and improve supportive care within Afghanistan's fragile health system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".