Diffusion magnetic resonance imaging-based surrogate marker in amyotrophic lateral sclerosis
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is the most prevalent type of motor neuron disease (MND) and is diagnosed with a delay from the first appearance of symptoms. Surrogate markers that may be used to detect pathological changes before a significant neuronal loss occurs and allow for early intervention with disease-modifying therapy techniques are desperately needed. Using water molecules that diffuse within the tissue and experience displacement on the micron scale, diffusion magnetic resonance imaging (MRI) is a promising technique that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, axonal density, order, and myelination. Diffusion tensor imaging (DTI) is the primary diffusion MRI technique used to evaluate the pathogenesis of ALS. Neurite orientation dispersion and density imaging (NODDI), diffusion kurtosis imaging (DKI), and free water elimination DTI (FWE-DTI) are only a few of the approaches that have been developed to overcome the shortcomings of the diffusion tensor technique. This article provides a summary of these methods and their potential as surrogate markers for detecting the onset of ALS at an early stage.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".