Intronic FGF14 GAA repeat expansions impact progression and survival in multiple system atrophy
Bibliographic record
Abstract
Acknowledgements: We thank the participants and their families for their essential help with this work. We thank Dr Paul Lockhart for a kind gift of amplicons with FGF14 (GAA)n expansions in the pathological range. We also thank the Interdisciplinary Center for Biotechnology Research (ICBR) at University of Florida for contributing to long-read completion. Tissue samples and associated clinical and neuropathological data were supplied by the Queen Square Brain Bank, IBB-NeuroBiobank, CIBERNED Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas-Instituto de Salud Carlos III, the Multiple Sclerosis Society Tissue Bank (funded by the Multiple Sclerosis Society of Great Britain and Northern Ireland, registered charity 207495). We also thank Dr Gilbert Bensimon, Dr Christine Payan, Professor Ammar Al-Chalabi and the NNIPPS Consortium for access to the NNIPPS data. We also thank PROSPECT-M UK study for access to samples and data from clinically diagnosed MSA patients.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".