Comparison of the single simple question and the patient acceptable symptom state in myasthenia gravis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The single simple question (SSQ) is a simple and validated question asking what percentage of normal a patient feels with respect to their myasthenia gravis (MG), with 100% being normal. Patient acceptable symptom states (PASS) are based on a dichotomous 'Yes' or 'No' response, asking whether a patient is satisfied overall with their current status and thus measures holistic satisfaction with their MG state. Both are patient-reported self-assessments but assess different dimensions of MG. The objective was to determine thresholds for the SSQ when patients with MG achieve an acceptable PASS status. METHODS: A retrospective chart review was performed of consecutive MG patients attending a neuromuscular clinic, and SSQ and PASS responses, demographic, clinical and serological characteristics and disease severity by the MG impairment index were extracted. RESULTS: One hundred and fifty-seven consecutive patients were identified: 43 (27.4%) patients responded 'No' to the PASS question. Between the PASS 'Yes'/'No' groups, only SSQ (87.5 ± 13.4 vs. 52.3 ± 23.3; P < 0.001) and MG impairment index scores (9.2 ± 10.3 vs. 29.6 ± 16; P < 0.001) were significantly different. The receiver operating characteristic curve for PASS and SSQ had an area under the curve of 0.92 ± 0.024 (confidence interval 0.872-0.965, P < 0.001). An SSQ score ≥72.5% had 84.2% sensitivity and 86% specificity to classify patients as PASS positive. CONCLUSION: The PASS and SSQ patient-reported outcomes are closely associated and a SSQ threshold ≥72.5% predicts an acceptable MG state. Other demographic and disease-related factors did not influence the PASS response in this study.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".