L07 The functional rating taskforce for pre-huntington's disease: development of the furst-21 scale
Bibliographic record
Abstract
Background There is a need for clinical scales specifically designed to measure the earliest clinical manifestations of Huntington disease (HD) and to track early changes in clinical symptoms in HD gene expansion carriers. Such a measurement tool could be used to evaluate the effect of novel therapies early in the course of disease. Aims The Functional Rating Scale Taskforce for pre-HD (FuRST-pHD) is a multinational, multidisciplinary collaboration to develop a valid functional rating scale to assess changes in symptom severity in prodromal (prHD) or early manifest HD gene expansion carriers. Methods FuRST-pHD has established a process for scale development using input from numerous sources, including HD individuals and companions, experts from a variety of fields, as well as from data mining of ongoing observational studies in HD. FuRST-pHD utilised an iterative process in which changes to items are made based on empirical evidence obtained during field testing in prHD and early HD individuals, utilising various types of analyses, including Item Response and Rasch analyses, factor analysis, correlations and descriptive analyses, as well as clinical judgement. The criteria for item reduction and modification include assessment of item discrimination, relevance and response range, item redundancy, and convergent validity. Results A total of 115 structured interview questions were developed to assess the presence and severity of motor, cognitive and psychiatric symptoms, as well as day-to-day functioning. Following multiple testing iterations in 784 prHD/early HD participants, a 40-item FuRST V1.0 was subsequently tested in prHD/early HD gene carriers (n=97), of which 21 items have been retained for inclusion in the primary scale (FuRST-21). Conclusions FuRST-21 is a structured interview that shows promise as a tool to measure symptoms in prHD and early HD. Validation of FuRST-21 is currently in the planning stages.
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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 | 0.010 | 0.017 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".