Gold Coast diagnostic criteria: Implications for <scp>ALS</scp> diagnosis and clinical trial enrollment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it