Time to achieve a patient acceptable symptom state in myasthenia gravis
Bibliographic record
Abstract
Introduction The patient acceptable symptom state (PASS) is a reliable way to characterize a patient’s satisfaction with their disease state in a “Yes”/“No” dichotomous manner. There is limited data on the time required to reach an acceptable state in Myasthenia Gravis (MG). We aimed to determine the time to reach a first PASS “Yes” response in patients at MG diagnosis and a PASS “No” status, and also to determine the influence of various factors on this time. Methods We performed a retrospective study of patients diagnosed with myasthenia gravis who had an initial PASS “No” response and defined the time to reach a first PASS “Yes” by Kaplan–Meier analysis. Correlations were made between demographics, clinical characteristics, treatment and disease severity, using the Myasthenia Gravis Impairment Index (MGII) and Simple Single Question (SSQ). Results In 86 patients meeting inclusion criteria, the median time to PASS “Yes” was 15 months (95% CI 11–18). Of 67 MG patients who achieved PASS “Yes,” 61 (91%), achieved it by 25 months after diagnosis. Patients who required only prednisone therapy achieved PASS “Yes” in a shorter time with a median of 5.5 months (p = 0.01). Very-late-onset MG patients reached PASS “Yes” status in a shorter time (HR = 1.99, 95% CI 0.26–2.63; p = 0.001). Discussion Most patients reached PASS “Yes” by 25 months after diagnosis. MG patients who only required prednisone and those with very-late-onset MG reach PASS “Yes” in shorter intervals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".