Huntington’s Disease Integrated Staging System (HD-ISS): A Novel Evidence-Based Classification System For Staging
Bibliographic record
Abstract
ABSTRACT Background Despite the monogenic autosomal dominant nature of Huntington’s disease (HD), the current research paradigm is still based on overt clinical phenotypes and does not address disease pathobiology and biomarkers that are evident decades before functional decline. A new research framework is needed to standardize clinical research and enable interventional studies earlier in the course of HD. Methods The HD Regulatory Science Consortium (HD-RSC), a precompetitive Critical Path Institute initiative that includes 37 member organizations, created the Regulatory Science Forum working group (RSF), which includes industry and academic representatives. To generate a new evidenced-based HD Integrated Staging System (HD-ISS) using a formal consensus methodology, the RSF considered prognostic biomarkers, signs, and symptoms of HD, and performed empirical data analysis. We used observational data to calculate healthy-control-based landmark variable cut-offs for Stage classification and to internally validate the framework. Findings The HD-ISS starts with Stage 0, which comprises individuals with ≥ 40 cytosine-adenine-guanine repeats (CAG) in the huntingtin gene ( HTT ), before detectable indications of disease. We concluded that detectable HD progression is verified with measurable indicators of underlying pathophysiology (Stage 1), proceeds to a detectable clinical phenotype (Stage 2), and continues to a decline in function (Stage 3). Operationally, individuals can be unambiguously classified into Stages 1-3 based on CAG-independent thresholds of landmark assessments. Both cross-sectional status and longitudinal HD-ISS Stage progress align with HD natural history, and Stage transitions accelerate as CAG increase. Interpretation The HD-ISS encompasses the full course of HD starting at birth, defined by the presence of the genetic expansion. This new framework aims to standardize language for clinical research and its immediate use will enable further validation. The HD-ISS provides structure to harmonize clinical study populations and facilitates the clinical assessment of interventions earlier in HD to prevent or slow disease progression. Funding CHDI Foundation Inc.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".