Gold Coast diagnostic criteria: Implications for <scp>ALS</scp> diagnosis and clinical trial enrollment
Bibliographic record
Abstract
Diagnostic criteria for amyotrophic lateral sclerosis (ALS) are complex, incorporating multiple levels of certainty from possible through to definite, and are thereby prone to error. Specifically, interrater variability was previously established to be poor, thereby limiting utility as diagnostic enrollment criteria for clinical trials. In addition, the different levels of diagnostic certainty do not necessarily reflect disease progression, adding confusion to the diagnostic algorithm. Realizing these inherent limitations, the World Federation of Neurology, the International Federation of Clinical Neurophysiology, the International Alliance of ALS/MND Associations, the ALS Association (United States), and the Motor Neuron Disease Association convened a consensus meeting (Gold Coast, Australia, 2019) to consider the development of simpler criteria that better reflect clinical practice, and that could merge diagnostic categories into a single entity. The diagnostic accuracy of the novel Gold Coast criteria was subsequently interrogated through a large cross-sectional study, which established an increased sensitivity for ALS diagnosis when compared with previous criteria. Diagnostic accuracy was maintained irrespective of disease duration, functional status, or site of disease onset. Importantly, the Gold Coast criteria differentiated atypical phenotypes, such as primary lateral sclerosis, from the more typical ALS phenotype. It is proposed that the Gold Coast criteria should be incorporated into routine practice and clinical trial settings.
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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 | 0.573 | 0.759 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".