Gold Coast criteria expand clinical trial eligibility in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Abstract Introduction/Aims Consensus criteria to formalize the diagnosis of amyotrophic lateral sclerosis (ALS) and refine clinical trial populations have evolved. The recently proposed Gold Coast consensus criteria are intended to simplify use and increase sensitivity. We aimed to evaluate the potential impact of these criteria on clinical trial eligibility. Methods We performed a single‐center, retrospective study of people diagnosed with ALS between 2016 and 2021 to determine the numbers of those meeting Gold Coast, revised El Escorial (rEEC) criteria, and Awaji criteria. We identified the proportion of those who would have been eligible for participation in three major ALS clinical trials if Gold Coast were used in place of rEEC definite/probable criteria. (rEEC D/P). Results Two hundred six people with ALS were included in our study. 48.5% met Gold Coast criteria but not rEEC D/P. Using the Gold Coast criteria would result in higher rates of clinical trial eligibility after other inclusion criteria were met: 95.2% vs 42.5% ( P < .001) in a phase III study of riluzole; 100% vs 31.0% ( P = .002) in a phase III study of edaravone; and 95.6% vs 45.3% ( P < .001) in an ongoing phase III study of sodium phenylbutyrate and taurursodiol. The sensitivity of the Gold Coast criteria (96.1%; 95% confidence interval [CI], 92.2%‐98.2%) was significantly higher than that of rEEC D/P (47.6%; 95% CI, 40.6%‐54.6%; for difference, χ 2 = 117.6; P < .001). Discussion Until robust biomarkers are available to diagnose ALS, consensus diagnostic criteria remain necessary. Gold Coast criteria would expand research and clinical trial eligibility and improve external validity of clinical trial results.
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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 | 0.291 | 0.379 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".