Quality of Life for Hong Kong Chinese Patients with Advanced Gynecological Cancers in the Palliative phase of Care: A Cross-Sectional Study
Bibliographic record
Abstract
This study investigated the quality of life (QOL) of Hong Kong Chinese patients with advanced gynecological cancers (AGC). A cross-sectional study was conducted with 53 consecutive patients with AGC who were admitted to a university-based palliative care unit. The assessment tools utilized were: the McGill quality of life questionnaire for Hong Kong Chinese (MQOL-HK); the hospital anxiety and depression scale (HADS); the Palliative Performance Scale (PPS); and the psychosocial adjustment to illness scale (PAIS), sexual relationships subscale. The mean total score of the MQOL-HK was 4.63 +/- 1.94, within which the physical domain scored the worst (mean=3.99, SD=2.15, range: 0-7). Depression symptoms were common (62 percent). The median PPS was 40 percent. Younger age, higher HADS depression scores, and higher HADS anxiety scores were significantly correlated with poorer QOL. Furthermore, younger age and depression were significant predictors for a worse MQOL-HK score. In conclusion, Chinese patients with AGC have a relatively poor QOL, especially in the physical domain and in terms of depression symptoms. Age and depression symptoms are the most important factors affecting QOL. Proper identification of physical symptoms and depression symptoms, along with appropriate treatments, are important for improving QOL for 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".