The development of a new patient-reported outcome measure in recessive ataxias: The Person-Reported Ataxia Impact Scale
Bibliographic record
Abstract
Abstract Introduction. Autosomal recessive cerebellar ataxias (ARCAs) are inherited neurological disorders that can affect both thecentral and peripheral nervous systems. To assess the effects of interventions according to the perception of people affected, patient-reported outcome measures (PROMs) must be available. Objective. This paper presents the development process of the Person-Reported Ataxia Impact Scale (PRAIS), a new PROM in recessive ataxias, and the documentation of its content validity, interpretability, and construct validity (structural and discriminant). Patients and methods.The development followed the PROMIS framework and the Food and Drug Administration guidelines. A mixed-method study design was used to develop the PROM. A systematic review of the literature, semistructured interviews, and discussion groups was conducted to constitute an item pool. Experts’ consultation helped formulate items, and the questionnaire was sent online to be completed by people affected. Statistical analyseswere performed to assess the structural and discriminant validity. Results. A total of 125 people affected by recessive ataxia completed the questionnaire. The factoranalysis confirmed the three components: physical functions and activities, mental functions, and social functions. The statistical analysis showed that it can discriminate between stages of mobility and level of autonomy. It showed very good levels of internal consistency (0.79 to 0.89). Conclusion. The Person-Reported Ataxia Impact Scale (PRAIS) is a 38-item questionnaire that assesses the manifestations and impacts of the disease according to the perception of people affected by recessive ataxia. It can be used in 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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".