Current Recommendations for Patient-Reported Outcome Measures Assessing Domains of Quality of Life in Neurofibromatosis Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: To review and recommend patient-reported outcome (PRO) measures assessing multidimensional domains of quality of life (QoL) to use as clinical endpoints in medical and psychosocial trials for children and adults with neurofibromatosis (NF) type 1, NF2, and schwannomatosis. METHODS: The PRO working group of the Response Evaluation in Neurofibromatosis and Schwannomatosis (REiNS) International Collaboration used systematic methods to review, rate, and recommend existing self-report and parent-report PRO measures of generic and disease-specific QoL for NF clinical trials. Recommendations were based on 4 main criteria: patient characteristics, item content, psychometric properties, and feasibility. RESULTS: The highest-rated generic measures were (1) the Pediatric Quality of Life Inventory (PedsQL) Generic Core Scales for NF clinical trials for children or for children through adults, (2) the Functional Assessment of Cancer Therapy-General for adult medical trials, and (3) the World Health Organization Quality of Life-BREF for adult psychosocial trials. The highest-rated disease-specific measures were (1) the PedsQL NF1 Module for NF1 trials, (2) the NF2 Impact on Quality of Life Scale for NF2 trials, and (3) the Penn Acoustic Neuroma Quality of Life Scale for NF2 trials targeting vestibular schwannomas. To date, there are no disease-specific tools assessing multidimensional domains of QoL for schwannomatosis. CONCLUSIONS: The REiNS Collaboration currently recommends these generic and disease-specific PRO measures to assess multidimensional domains of QoL for NF clinical trials. Additional research is needed to further evaluate the use of these measures in both medical and psychosocial trials.
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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 | 0.494 | 0.691 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".