Can we find early phase biomarkers for ALS: What are the prospects and challenges?
Bibliographic record
Abstract
Age-related neurological disorders such as ALS (Lou Gehrig’s disease), Parkinson’s disease, and Alzheimer’s disease have few truly effective treatment options. At best, these may slow the inexorable disease progression without providing a cure. Part of the problem with therapeutic approaches may arise due to the stage at which these diseases are detected, particularly the sporadic forms. In most cases, early signs and symptoms may be insidious, thus hiding the significant damage done to the areas of the nervous system impacted prior to any firm clinical diagnosis. This situation appears to necessitate the development of earlier detection methods for “biomarkers” that might allow for much earlier phase disease state treatments that might serve to significantly slow or even halt disease progression. Currently, most biomarkers in use serve primarily as aids to disease diagnosis, at which point there are no successful treatment options. In contrast, a search for more effective early treatment options would need to identify characteristic and specific molecular signatures of disease onset and progression using methods that are simple, such as blood-based analytical assays, relatively cheap, and crucially minimally invasive.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".