Development and validation of a patient-centered, meningioma-specific quality-of-life questionnaire
Bibliographic record
Abstract
OBJECTIVE: Meningiomas can have significant impact on health-related quality of life (HRQOL). Patient-centered, disease-specific instruments for assessing HRQOL in these patients are lacking. To this end, the authors sought to develop and validate a meningioma-specific HRQOL questionnaire through a standardized, patient-centered questionnaire development methodology. METHODS: The development of the questionnaire involved three main phases: item generation, item reduction, and validation. Item generation consisted of semistructured interviews with patients (n = 30), informal caregivers (n = 12), and healthcare providers (n = 8) to create a preliminary list of items. Item reduction with 60 patients was guided by the clinical impact method, multiple correspondence analysis, and hierarchical cluster analysis. The validation phase involved 162 patients and collected evidence on extreme-groups validity; concurrent validity with the SF-36, FACT-Br, and EQ-5D; and test-retest reliability. The questionnaire takes on average 11 minutes to complete. RESULTS: The meningioma-specific quality-of-life questionnaire (MQOL) consists of 70 items representing 9 domains. Cronbach's alpha for each domain ranged from 0.61 to 0.91. Concurrent validity testing demonstrated construct validity, while extreme-groups testing (p = 1.45E-11) confirmed the MQOL's ability to distinguish between different groups of patients. CONCLUSIONS: The MQOL is a validated, reliable, and feasible questionnaire designed specifically for evaluating QOL in meningioma patients. This disease-specific questionnaire will be fundamentally helpful in better understanding and capturing HRQOL in the meningioma patient population and can be used in both clinical and research 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 | 0.013 | 0.019 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".