Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: ObservationalConsensus signal: Observational
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.012
- Threshold uncertainty score
- 0.603
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.288 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Abstract The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype ( p = 7.18 × 10 −4 ) or temporal stage ( p = 3.96 × 10 −5 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.
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.
The record
- Venue
- Nature Communications
- Topic
- Amyotrophic Lateral Sclerosis Research
- Field
- Medicine
- Canadian institutions
- Parkwood InstituteSt Joseph's Health CareMcGill UniversityUniversity of British ColumbiaToronto Western HospitalSunnybrook Health Science CentreJewish General HospitalBaycrest HospitalUniversity of TorontoWestern UniversityUniversité LavalOccupational Cancer Research CentreHealth Sciences CentreUniversity Health Network
- Funders
- EPSRC Centre for Doctoral Training in Medical ImagingEconomic and Social Research CouncilEngineering and Physical Sciences Research CouncilNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchGenentechAssociazione Italiana Ricerca AlzheimerIXICOH. Lundbeck A/SServierUniversity College London Hospitals NHS Foundation TrustEisaiWolfson FoundationBrain Research TrustWeston Brain InstituteNational Institute on AgingNational Institute for Health and Care ResearchNorthern California Institute for Research and EducationPfizerBiogenBioClinicaF. Hoffmann-La RocheAlzheimer's SocietyWellcome TrustUniversity of Southern CaliforniaNovartis Pharmaceuticals CorporationU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbNational Institutes of HealthRosetrees TrustEuropean CommissionAlzheimer's Disease Neuroimaging InitiativeMedical Research CouncilMeso Scale DiagnosticsAlzheimer's AssociationMichael J. Fox Foundation for Parkinson's ResearchFoundation for the National Institutes of Health
- Keywords
- NeurodegenerationDiseaseInferenceFrontotemporal dementiaPrecision medicinePhenotypeBiologyGenetic heterogeneityNeuroscienceComputational biologyDementiaBiomarker discoveryBioinformaticsMedicineComputer scienceGeneticsArtificial intelligencePathologyGene
- Has abstract in OpenAlex
- yes