Living with cancer: ?Good? days and ?bad? days?What produces them?
Bibliographic record
Abstract
BACKGROUND: To determine the impact of care on quality of life (QOL), or to detect a change in QOL over time, measures of QOL must remain stable when QOL is stable (test-retest reliability) and change when QOL changes (responsiveness). This study addresses these issues for the McGill Quality of Life Questionnaire (MQOL). Unlike other studies that use disease status to indicate whether QOL has remained stable or changed, in this study the patient determines QOL stability or change. The authors also sought to clarify the determinants of "good" and "bad" days for oncology patients. METHODS: Patients attending an oncology outpatient clinic or who were being treated by a palliative care service were asked to complete MQOL 4 times: on days they judged to be "good," "average," and "bad" and 2 days after the first completion. They also were asked to directly rate the change in their QOL during the intervals between MQOL completion and to report the most important determinants of their good and bad days. RESULTS: The test-retest reliability of MQOL as measured by an intraclass correlation coefficient ranged from 0.69 to 0.78. All MQOL scores were significantly different on good, average, and bad days, except for the support subscale, in both clinical settings. Five domains were determinants of QOL: physical symptoms, physical functioning, psychologic well-being, existential well-being, and relationships. CONCLUSIONS: MQOL's reliability and responsiveness suggest it can be used to determine changes in the QOL of groups. The results allow interpretation of changes in MQOL scores with respect to meaning of the change to oncology patients. This in turn is helpful to determine the sample size required in future studies. Some of the domains important to the QOL of oncology patients are not included in widely used measures of QOL.
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 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) | 0.000 | 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) | 0.006 | 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".