P.047 Implications of Gold Coast Criteria in diagnosis of amyotrophic lateral sclerosis in a large subspecialty clinic
Bibliographic record
Abstract
Background: Criteria to formalize the diagnosis of amyotrophic lateral sclerosis (ALS) and refine clinical trial populations have evolved. The recently proposed Gold Coast criteria (GCC) are intended to simplify use and increase sensitivity. We evaluated sensitivity of GCC and potential impacts on therapeutic trial enrollment. Methods: We performed a single center retrospective study including patients diagnosed with ALS between 2016 – 2021. We determined criteria met at diagnosis according to revised El Escorial (rEEC), Awaji (AC) and GCC. We compared sensitivity and examined impacts GCC would have on enrollment in landmark ALS trials. Results: We included 203 people with ALS. Sensitivity of GCC (96.1%, 95% confidence interval [CI] = 92.2-98.2%) was significantly higher than rEEC (89.8%, 95% CI 84.6-93.4%, χ2 = 5.3, p = 0.01) and AC (89.3%, 95% CI 84.1-93.0%, χ2 = 6.1, p = 0.006). GCC was more sensitive than clinically definite or probable rEEC (47.6%, 95% CI 40.6-54.6%, χ2 = 117.6, p = < 0.001) and use would result in increased eligibility in landmark therapeutic trials. Conclusions: GCC are more sensitive than rEEC and AC at time of diagnosis in ALS. Use of GCC in our population would expand clinical trial participation and make results more widely generalizable.
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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 | 0.101 | 0.243 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".