Bibliographic record
Abstract
Over the past 120 years, mortality associated with myasthenia gravis (MG) has steadily decreased while the incidence of MG has increased. While mortality due to MG has been ≤5 % for at least the past 25 years, the prevalence of MG has increased. This increase in prevalence of MG may be due, in part, to improvements in diagnostics but also to an aging global population and immunosenescence as the largest increases in MG prevalence have been in patients ≥65 years old. In fact, a "very late-onset" subtype of MG has been proposed for patients diagnosed at or after age 65 years. These patients are predominantly anti-AChR antibody positive and thymoma negative. Preferred therapeutic options differ based on age at MG onset. Immunosenescence may play a role not only in MG etiology but also in the increased susceptibility of MG patients to infection. Immunosuppressive effects of MG therapies can also increase vulnerability to infection. Despite the improvements in MG treatment, mortality in MG patients remains higher than in the non-MG population. This is partly due to increased vulnerability to infection but also due to infection acting as a precipitating factor for MG exacerbation or crisis. The increased infection risk inherent with MG and the increased risk resulting from some therapies calls for increased diligence in monitoring and treating infections in MG patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".