Bibliographic record
Abstract
To the Editor: In the recent review of the pathobiology of amyotrophic lateral sclerosis (ALS) by Strong et al (1), tau pathology is described as being characteristic of the neuropathology of ALS, ALS with dementia, and frontotemporal lobar degeneration (FTLD)-motor neuron disease (MND)-type. The literature of the neuropathology of these diseases and our own extensive experience do not support this conclusion (2-5). Abnormal protein aggregates, either within neurons or glia or in the extracellular space, are characteristic of most neurodegenerative disorders. Several seemingly diverse disorders are now linked by the pathologic protein that defines their molecular pathology; thus, several diseases may now be classified as tauopathies, synucleinopathies, prion diseases, or trinucleotide repeat disorders. However, there remain a number of neurodegenerative diseases that have ubiquitinated neuronal inclusions, but the pathologic protein of the inclusion remains unknown and their nosology remains tentative. Motor neuron disease, motor neuron disease with dementia (MNDD), and frontotemporal lobar degeneration with motor neuron disease-type inclusions (FTLD-MND-type) are clinically and neuropathologically overlapping disorders and fall into this category of neurodegenerative disease with ubiquitin-positive inclusions (2-5).
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.021 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".