Absence of consensus in diagnostic criteria for familial neurodegenerative diseases
Bibliographic record
Abstract
BACKGROUND: A small proportion of cases seen in neurodegenerative conditions such as amyotrophic lateral sclerosis (ALS), Parkinson's disease and Alzheimer disease are familial. These familial cases are usually clinically indistinguishable from sporadic cases. Identifying familial cases is important both in terms of clinical guidance for family members and for gene discovery. METHOD: Surveys assessing the definition of familial amyotrophic lateral sclerosis (FALS) were completed by clinicians with an interest in ALS. RESULTS: 95 surveys were completed by respondents from 15 countries. A third of total respondents stated that they thought that neurologists were using the same definition for FALS (33.3%, 30). No consensus was achieved among clinicians when provided with five different definitions for FALS. However, the preferred definition was 'a patient with ALS with either a first or second degree relative also with ALS' (37.8%, 31). CONCLUSION: There is no consensus on a standard definition for FALS among clinicians. It is likely that similar inconsistencies apply to other conditions, such as Parkinson's disease and Alzheimer disease, in which both familial and sporadic diseases occur. Inconsistent classification could hinder gene discovery.
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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 | 0.136 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".