Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The optimal design and outcome measures for preventive clinical trials in neurodegenerative diseases are unknown. OBJECTIVE: To examine measures that may be associated with disease in the largest cohort ever recruited of prediagnosed individuals carrying the gene expansion for Huntington disease (HD). DESIGN: The Predict-HD study is a multicenter observational research study in progress at 17 sites in the United States, 4 in Canada, and 3 in Australia. SETTING: Genetics and HD outpatient clinics. PARTICIPANTS: Five hundred five at-risk individuals who had previously undergone elective DNA analyses for the CAG expansion in the HD gene (predictive testing) and did not currently have a clinical diagnosis of HD. MAIN OUTCOME MEASURES: Basal ganglia volumes on magnetic resonance images, estimated probability of diagnosis (based on CAG repeat length), performances on 21 standardized cognitive tasks, total scores on 3 scales of psychiatric distress, and motor diagnosis based on the Unified Huntington's Disease Rating Scale. RESULTS: Several variables showed progressive decline as the diagnostic ratings advanced toward manifest disease. Estimated probability of diagnosis was associated with Unified Huntington's Disease Rating Scale prediagnostic stages and varied from 15% in persons with no motor abnormalities to nearly 40% in those with abnormalities suggestive of probable disease. Striatal volumes, cognitive performances, and even psychiatric ratings declined significantly with motor manifestations of disease. CONCLUSIONS: The documentation of biological and refined clinical markers suggests several clinical end points for preventive clinical trials. Longitudinal study is critical to further validate possible markers for prediagnosed HD.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| 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 | 0.000 |
| 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)
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 it