Validation of a Quality-of-Life Questionnaire for Patients with Pituitary Adenoma
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 of patients with pituitary adenoma. METHODS: A patient-centred iterative process, involving a literature review and focus group interviews with 84 patients, was used to develop a preliminary 106-item questionnaire and to validate it. The final questionnaire comprised the 30 most important items chosen by 20 patients and 17 items considered important by health care professionals. For assessment of its validity and reliability, 55 patients were asked to complete the final questionnaire, RAND-36, FACT- G/FACT-Br, and Karnofsky Performance Scale (KPS). Concurrent validity of the results of our questionnaire with those of the other instruments was assessed with Pearson correlation coefficients. Known-group validity for the scores of extreme groups was assessed with a Student's t test. Test-retest reliability was determined with Pearson correlation coefficients and a Student's t test for two sets of scores obtained one month apart. RESULTS: Forty-seven (85.5%) of the 55 questionnaires were completed. Our questionnaire was well correlated with the RAND-36, the FACT-Br, the FACT-G, and the KPS in the general health domain, but not with the KPS overall. Extreme groups (n = 20) were significantly different. Test-retest reliability (n = 24) was 0.88, and scores one month apart were not significantly different. CONCLUSION: Our patient-centred health-related quality of life questionnaire developed for patients with pituitary adenoma had good validity and reliability. This questionnaire could be used as a patient-centred outcome measure in clinical trials and for assessment of disease progression.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".