Bibliographic record
Abstract
BACKGROUND: Cancer is the leading cause of death in Korean adults. A good quality of life for patients at end life can control pain and symptoms and help maintain well-being. OBJECTIVE: The aim of this study was to measure quality of life in end-stage cancer patients using the Korean version of the McMaster Quality of Life (K-MQOL). METHODS: The K-MQOL was administered to adult end-stage cancer patients from 4 Korean university hospitals. We hypothesized quality-of-life differences between participants by daily activity level, number of symptoms, and participant status (alive or not) at end of study. RESULTS: Participants' mean age was 49.2 years, and 74.5% were men. As hypothesized for discriminant validity, Pearson correlation coefficients among the K-MQOL were less than 0.4, with the exceptions of emotion (0.25-0.52) and cognition (0.33-0.51). A higher Eastern Cooperative Oncology Group Performance States Rating score was significantly associated with a lower quality of life (F = 2.840, P = 0.034). The mean score of those within 21 days of death was significantly lower than that of patients who were alive at the end of the study (t = -2.48, P = .04). Patients with a smaller number of symptoms other than pain had significantly higher quality-of-life scores than did those with more symptoms (F = 5.059, P = .004). CONCLUSIONS: The K-MQOL provided reliable and valid scores of quality of life in end-stage cancer patients. IMPLICATIONS FOR PRACTICE: Assessing end-stage cancer patients' quality of life helps to identify each patient's condition and aspects that could benefit from nursing care. We anticipate that the K-MQOL will be useful for patient assessment in clinical and community settings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".