Current pathways for epidemiological research in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disease. The current status of the epidemiology, challenges to its study, and novel study design options are discussed in this paper. We focus on recent results from large-scale population based prospective studies, case-control studies and population based registries, risk factors, and neuropathologic findings in chronic traumatic encephalomyelopathy. We identify areas of interest for future research, including time-trends in the incidence and prevalence of ALS; the meaning of lifetime risk; the phenotypic description of ALS; the definition of familial versus sporadic ALS, syndromic aspects of ALS; specific risk factors such as military service, life style factors such as smoking, the use of statins, and the presence of β-N-methylamino-L-alanine (BMAA), an excitotoxic amino acid derivative possibly produced by cyanobacteria found in almost every terrestrial and aquatic habitat; the emergence and disappearance of an endemic ALS in areas of the Pacific; and gene-environment interactions in the etiology of ALS. To move the epidemiology forward, we suggest using well-characterized cohorts of newly diagnosed ALS patients to identify risk and prognostic factors; storing biological material for future studies; building on the National ALS Registry as a resource of future studies; working in multidisciplinary consortia; and addressing the possible early life etiology of ALS.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".