Development of a Concise QOL Questionnaire for Brain Tumor Patients
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to develop and validate a self-administered questionnaire to measure the health-related quality of life (QOL) of patients with brain cancer. We wanted to assess both core and disease-specific concerns in a single, easy-to-use instrument, thus promoting concision and clinical utility. The questionnaire departs from its predecessors in that it was designed for- and validated among French speaking Canadians. METHODS: A focus group of health professionals was used to develop items for the questionnaire, which was later validated with 105 patients suffering from brain cancer. The underlying structure of the questionnaire was investigated using principal component analysis and confirmed using a principal factor analysis. RESULTS: The final version of the questionnaire contains 30 items. Seven multi-item scales, tapping into distinct dimensions of QOL, were uncovered (i.e., functional well-being, symptom severity/fear of death, social support/acceptance of disease, autonomy in personal care, digestive symptomatology, neurocognitive function, and pain). Assessment of reliability revealed elevated internal consistency for each of the seven scales (Cronbach coefficient alpha .65), whereas known-groups validity (anchor-based approach) revealed that the different dimensions uniquely discriminated between patients with different functional levels (Karnofsky Performance Scores) and clinical status (exposure to neurosurgery, radiotherapy, and use of chemotherapy and anticonvulsants). CONCLUSION: Our QOL questionnaire, the Sherbrooke Neuro-Oncology Assessment Scale, or SNAS, taps into both core and disease-specific issues relevant to neuro-oncology patients. It has good validity and reliability, and clearly reflects the multidimensional nature of QOL. Depending on the research focus, it may be used in clinical trials to track the impact of disease and/or treatment on satisfaction, functional status, and general well-being.
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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 | 0.004 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".