Multicentre quality control evaluation of different biomarker candidates for amyotrophic lateral sclerosis
Bibliographic record
Abstract
Abstract Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that mainly causes degeneration of the upper and lower motor neurons, ultimately leading to paralysis and death within three to five years after first symptoms. The pathological mechanisms leading to ALS are still not completely understood. Several biomarker candidates have been proposed in cerebrospinal fluid (CSF). However, none of these has successfully translated into clinical routine. Part of the reason for this failure to translate may relate to differences across laboratories. For this reason, several of the most commonly used ALS biomarker candidates were evaluated on clinically well-defined ALS samples from six European centres in a multicentre sample-collection approach with centralized sample processing. Results showed that phosphorylated neurofilament heavy chain differentiated between ALS and control cases in all centres. We therefore propose that measurement of phosphorylated neurofilaments in CSF is the most promising candidate for translation into the clinical setting and might serve as a benchmark for other biomarker candidates.
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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 | 0.071 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".