Developing digital health technologies for frontotemporal degeneration
Bibliographic record
Abstract
Frontotemporal degeneration (FTD) is a rare neurodegenerative disease in which patients can present with cognitive, behavioral, motor, and speech impairment. Currently, there are no approved therapies available to slow or halt disease progression. Detection and monitoring of patient symptoms is challenging for this heterogeneous disease and has negatively impacted progress in FTD clinical trials. Rapid technological advancements can promote the development of digital health technologies (DHTs) capable of capturing even the most subtle clinical impairments. DHTs are computing platforms being designed to measure meaningful aspects of disease onset and progression. Here we present some of the numerous tools currently being developed to measure changes in the functional domains that become impaired in FTD, challenges faced by developers, and a proposed roadmap for developing fit-for-purpose DHTs that will aid in the development of effective therapies for FTD. HIGHLIGHTS: DHTs are being developed to assess FTD onset and progression. Tool developers must overcome numerous challenges in creating effective applications. Guidance to tool developers aims to benefit FTD drug development and patient care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".