The future is precision medicine-guided diagnoses, preventions and treatments for neurodegenerative diseases
Bibliographic record
Abstract
Recent advances in machine-learning algorithms and artificial intelligence (AI) have provided a much-needed framework for data mining and compilation that will lead to the development of the most accurate diagnostics and therapeutic approaches.AI-driven predictive analytics based on neurodegenerative diagnostic measures together with health status (co-morbidities), genetics, environmental exposures, and lifestyle factors would provide an adaptive and harmonized toolbox for healthcare providers to most effectively treat complex, multi-factorial diseases.These algorithms can also mine massive datasets to predict response to treatment, as well as the risk of disease progression.Significant hurdles to overcome include data harmonization and sharing, as well as inclusivity in clinical trials.Numerous initiatives have been undertaken to bridge inconsistencies in nomenclature and provide easily accessible datasets.Now standard to the field is the NIA-AA Research Framework that biologically defines and clinically stages Alzheimer's disease (Jack et al., 2018).This provided consistent reporting across studies to generate more meaningful information and outcomes.Similar undertakings have occurred in the sub-sectors of reserve and resilience (Stern et al., 2022), astrocyte (Escartin, 2022) and microglial (Paolicelli et al., 2022) nomenclature, and omics taxonomy in Alzheimer's disease (Iturria-Medina et al., 2022), to name a few.Data accessibility has been streamlined through disease data sharing initiatives like the Alzheimer's Disease Neuroimaging Initiative (ADNI) consortium of universities and medical centers in the United States and Canada (Petersen et al., 2010; Alzheimer's Disease Neuroimaging Initiative, 2023), and the National Eye Institute (NEI) Data Commons (NEI Data Commons, 2023), a virtual platform to enable sharing and accessing vision research data and tools for data processing and analysis.While these data repositories are incredibly useful to drive new research findings, a focus on future integration and compilation across resources is essential.A useful model is standardization and harmonization of data collection and protocols across Alzheimer's Disease Research Centers (ADRCs).In a similar data harmonization effort, the
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".