Chronic stress, depression and personality type in patients with myasthenia gravis
Bibliographic record
Abstract
Background and purpose Stress is a known risk factor for the onset and modulation of disease activity in autoimmune disorders. The aim of this cross‐sectional study was to determine any associations between myasthenia gravis (MG) severity and chronic stress, depression and personality type. Methods In all, 179 consecutive adult patients with confirmed MG attending the Neuromuscular Clinic between March 2017 and December 2017 were included. At baseline, patients were assessed clinically and they completed self‐administered scales for disease severity, perceived stress, depression and personality type. Results Higher disease severity [Myasthenia Gravis Impairment Index (MGII)] showed a moderate correlation with depression score (Beck's Depression Inventory, Second Edition, r = 0.52, P < 0.001) and a lower correlation with chronic stress (Trier Inventory for Assessment of Chronic Stress, r = 0.28, P = 0.001). Chronic stress scores were different according to personality types (anova, P = 0.02). The linear regression model with MGII score as the dependent variable showed R2 = 0.34, likelihood ratio chi‐squared 74.55, with P < 0.0001. The only variables that predicted disease severity were depression scores (P < 0.0001) and female sex (P = 0.003). Conclusions A significant association of MG severity with depression and chronic stress was found, as well as with female gender. These findings should raise awareness that the long‐term management of MG should address depression and potential stress and consider behavioural management to prevent stress‐related immune imbalance.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".