Bibliographic record
Abstract
Several neuroimaging modalities have been used with varying success to aid the clinical process of establishing the diagnosis of amyotrophic lateral sclerosis (ALS). By demonstrating evidence of occult upper motor neuron degeneration in vivo, a speedier and more definitive diagnosis in suspected cases could lead to earlier treatment and earlier enrollment in clinical trials. Findings compatible with ALS on routine MRI are not consistently found and are non-specific. Thus, routine anatomic imaging is useful in ruling out diseases that mimic ALS, but not in classification of new cases. Functional imaging techniques, such as PET and fMRI, have provided fascinating insights into the cortical functional reorganization that accompanies muscular weakness. PET and SPECT have revealed involvement of regions of the brain beyond the motor cortex, something not well appreciated by pathological examination. Of great need is a surrogate marker of therapeutic efficacy to make drug evaluation more efficient; neuroimaging, and magnetic resonance spectroscopy in particular, holds great promise in this regard in addition to helping us better understand the of neurodegeneration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".