Prediction of aspiration in myasthenia gravis
Bibliographic record
Abstract
Prediction of the risk of dysphagia and aspiration is important in the management of myasthenia gravis (MG). We assessed the ability of four bedside clinical tools to predict aspiration in 20 MG patients. Patients completed a self-directed questionnaire, underwent clinical neurological assessment and a bedside speech pathology assessment, and were assessed with the quantitative myasthenia gravis (QMG) score. The ability of these tools to predict aspiration was compared with the results of a modified barium swallow. Seven patients aspirated, 4 silently. The total self-directed questionnaire score, two specific questions on the self-directed questionnaire, the prediction based on clinical neurological assessment, and the QMG bulbar subset score all correlated with aspiration. The speech pathology prediction was highly sensitive but less specific. This pilot study shows that simple clinical tools can predict which MG patients are at risk of aspiration.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".