Family history of neurodegenerative disorders in patients with amyotrophic lateral sclerosis: population-based case–control study
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by progressive loss of motor neurons in the brain and spinal cord, leading to muscle weakness and death due to respiratory failure.1 Although most cases of ALS are sporadic, up to 10% of patients have a positive family history of ALS, usually associated with causative gene mutations.2 Latest evidence has shown that ALS is a complex multisystem disorder with extramotor involvement, namely, cognitive deficit and extrapyramidal dysfunction, expanding the clinical spectrum of the disease. Cognitive impairment is present in up to 50% of patients with ALS with different degrees of severity, and 15% of patients have a concomitant diagnosis of frontotemporal dementia (FTD).1 In fact, the clinical imaging and pathological and genetic overlap between ALS and FTD have led to the definition of an ALS/FTD continuum.1 Thus, one may speculate that a positive family history of neurodegenerative disorders (NDs) can be associated with ALS development. In this prospective case–control study, we aimed to describe the family aggregation of ND within the group of patients with ALS and to estimate the risk of ALS development in patients with a positive ND family history. ### Study population We included 496 adult Portuguese patients with ALS followed up from January 2015 to January 2018 in our ALS clinic in Lisbon. Patients with definite, probable laboratory-supported and …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".