The syndromes of frontotemporal dysfunction in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Amyotrophic lateral sclerosis is increasingly recognized to be a complex multisystems disorder both at the level of its pathobiology and in the breadth of non-motor manifestations that can accompany it. Paramount among these are disorders of frontotemporal function which can be associated with syndromes of behavioural, cognitive or executive dysfunction or manifest as a frontotemporal dementia (FTD). While these may occur in isolation and precede the development of motor deficits, more commonly they insidiously onset following the initial neuromuscular dysfunction. The earliest clinical manifestation is a loss of verbal fluency, disproportionate to impairments in oromotor control. There is good correlation between the presence of a syndrome of frontotemporal dysfunction and alterations in brain structure or function as identified with a wide variety of neuroimaging techniques and which reflect a frontotemporal lobar degeneration (FTLD). Although the cause(s) of this process remain to be defined, as with the clinical heterogeneity, there is likely to be significant biochemical heterogeneity. This includes alterations in tau protein metabolism which are present in a proportion of familial and sporadic ALS cases, as well as the western Pacific variant, and recently described alterations in the metabolism of the TAR DNA binding protein 43 (TDP-43).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.002 |
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".