Seropositivity in Myasthenia Gravis as a Predictor of Response to Therapeutic Plasma Exchange
Bibliographic record
Abstract
Background: In spite of the increasing availability of immunomodulatory treatments for myasthenia gravis (MG), little is known about factors that predict response to the treatment. We aim to study if the presence of acetylcholine receptor (AChR) antibodies may be one of the predictors of the response to the therapeutic plasma exchange (TPE) in patients with MG. Methods: The study was carried out in 78 patients with moderate to severe MG. They were divided into two groups: TPE group (62 patients) that included the patients who received TPE, and control (non-TPE) group who did not receive TPE and was only on medical treatment (16 patients). Patients in TPE group, then, were subdivided into sero-positive and sero-negative subgroups according to the presence or absence of AChR antibodies respectively. Scoring by Quantitative Myasthenia Gravis Scale (QMGS) was done before and 1 month after the TPE. Results: After 1 month of treatment, the change in QMGS was significantly higher in the TPE group than in the Non-TPE one (t = -6.406) (P < 0.0001). In the sero-positive group, the score of QMGS ranged initially from 19 to 34 (mean 26.48 ± 3.75) and after 1 month from 11 to 30 (mean 18.00 ± 4.45) with the median change of -32. The QMGS change was significantly greater in the sero-positive group than in the sero-negative one (t = -3.516) (P < 0.0001). Conclusions: As regards to the presence of AChR antibodies in patients with MG, both sero-positive and sero-negative groups responded to TPE but the sero-positive group had a better response. J Neurol Res. 2019;9(1-2):8-13 doi: https://doi.org/10.14740/jnr510
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".