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Record W3197864926 · doi:10.1136/jnnp-2021-ehdn.44

F01 Development of the huntington’s disease integrated staging system (HD-ISS)

2021· article· en· W3197864926 on OpenAlexaff
Sarah J. Tabrizi, Scott Schobel, Emily C. Gantman, Alexandra Mansbach, Beth Borowsky, Pavlina Konstantinova, Tiago Mestre, Ariana P. Mullin, Jennifer Panagoulias, Klaus Romero, Christopher A. Ross, Sudhir Sivakumaran, Emily C. Turner, Maurice Zauderer, Jeffrey D. Long, Cristina Sampaio

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsHuntington's diseaseDiseasePopulationObservational studyNeurodegenerationPsychologyMedicineNeuroscienceInternal medicine

Abstract

fetched live from OpenAlex

Background HD is an inherited autosomal dominant neurodegenerative disease. While there is biological certainty that individuals with a pathogenic expansion in the huntingtin gene (HTT) will develop the signs and symptoms of HD within a normal lifespan, this is not reflected in present terminology. Current staging methods do not address disease progression before an overt clinical phenotype, despite well-accepted biomarkers of neurodegeneration predating clinical diagnosis. Aims To propose a new HD framework, referred to as the HD-ISS, that comprises an HD biological research definition and evidence-based staging centered on prognostic biological, clinical, and functional landmarks. Methods This framework is the result of a formal consensus process by the HD-RSC’s Regulatory Science Forum (RSF), a working group of expert representatives from industry and academia. Observational data was employed to calculate ‘cut-offs’ using the extreme values in models of the control population to define the HD-ISS Stages and to evaluate the framework. Results The HD-ISS defines HD biologically as the presence of the expanded HTT gene. The HD-ISS landmarks demonstrate robust prognostic value to classify individuals into each Stage and data-driven landmark thresholds to define Stage boundaries that are not CAG-dependent. Individual study visits, participant Stage progression, and longitudinal models of Stage progression align with the natural history of HD and with increased CAG predicting accelerated transitions. Conclusions The RSF has developed a biological definition of HD and an evidence-based staging system that encompass the full course of the disease and are unconstrained by concepts such as ‘manifest’ or ‘pre-manifest.’ The HD-ISS is intended for research settings to allow clinical trials earlier in the disease course, and provides a new structure to anchor and harmonize clinical study populations. The immediate use of the HD-ISS will allow for further validation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.048
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.255

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.061
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0330.016

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.

Opus teacher head0.027
GPT teacher head0.247
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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