Quality of life and symptom burden in Chinese cancer patients: Results of a multicenter research project.
Bibliographic record
Abstract
e20695 Background: McGill QOL Questionnaire is a well known tool in the evaluation of both psychological and physical states in cancer patients. A number of translations have been made. It is widely used in western developed countries, yet little research has been performed in caner patients in Mainland China who speak Mandarin. Methods: In the first application of the MQOL-Chinese version, which was translated from the MQOL-English version, reliability, content and construct validity testing has been performed among the 126 cancer patients, screened from Tongji Hospital, a medical unit which can deliver comprehensive cancer palliative care. And then 170 patients from 4 different hospitals in China were randomly selected to fill in this scale. Results: First Test-retest reliability of the questionnaire is highly favorable, and all MQOL scores are internally consistent. Using a scientific Stratified Analysis, in the psychological domain, men report less negative emotions than females (question 5\6\8 P=0,002 \0,047\0,016), and patients with head and neck cancer have less negative emotions than those with thoracic cancer (question14 P=0.035).Furthermore, cancer patients less than 20 years old tend to be mentally stronger while people at 40-60 years old seem to have the greatest psychological burden (question6/7 P=0.034/0.017). In the semi-open questions in the second part about the physical domain, poor appetite is reported as the most troublesome problem (M=6.6/10, SD=2.77), while sleep problems rank the least distessing (M=4.2/10,SD=2.15).In the fourth part of the open questions, feeling encouraged is the most frequently mentioned, and physical and economic burden rank the 2nd and 3rd respectively. Conclusions: First of all, the MQOL-Chinese version is a valid instrument for measuring quality of life for cancer patients in Mainland China who speak Mandarin. Moreover, the outcomes above indicated that the psychological and physical domain need to receive more attention, and economic burden is also an urgent problem which needs to be improved among cancer patients especially in China, which is still a developing country with a huge population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".