Telephone consultation for myasthenia gravis care during the <scp>COVID</scp>‐19 pandemic: Assessment of a novel virtual myasthenia gravis index
Bibliographic record
Abstract
INTRODUCTION/AIMS: The aim of the study was to determine the association between the virtual Myasthenia Gravis Impairment Index (vMGII) with other patient-reported outcomes (PROs) of myasthenia gravis (MG) and its usefulness in telephone consultations with MG patients. METHODS: This was a retrospective case series in which vMGII score along with virtual Single Simple Question (vSSQ), virtual Patient-Acceptable Symptom State PASS (vPASS) response, and patient disease status based on Myathenia Gravis Foundation of America postintervention status were collected during telephone consultation along with the MGII, SSQ, and PASS responses during the preceding in-person clinic visits. RESULTS: In 214 patients, the mean difference of vMGII between the vPASS "Yes" and "No" groups was -14.2 ± 1.4 (95% confidence interval, -16.9 to -11.3; P < .001) with mean vMGII for vPASS "Yes" group being 6.4 ± 7.7 and vPASS "No" being 20.5 ± 11.5. A vMGII of 11.5 or higher predicted vPASS "yes" response with a sensitivity of 78.7% and specificity of 81.4%. A strong negative correlation was found between the vMGII and vSSQ (r = -.667; P < .001). The mean vMGII was 0.48 ± 1.42 for patients in remission, and 9.31 ± 10.93 for improved, 9.32 ± 8.79 for stable, and 22.58 ± 14.04 for worsened groups (P < .001). These associations were the same as those obtained during the preceding in-person clinic visit and the direction of change in MGII scores also indicated change in disease status. DISCUSSION: vMGII is an effective measure to assess an MG patient's disease status in telephone consultations and relates well with other PRO measures. The vMGII remains reliable for assessing MG disease status even with removal of the physical examination component.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".