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.
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.
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 | 0.000 |
| 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 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".