Bibliographic record
Abstract
Introduction Although some reports link Myasthenia Gravis to higher mortality, the evidence remains contradictory and unclear. Real-life data is limited primarily due to challenges in selecting control groups and mitigating bias. Additionally, a revised mortality assessment should be conducted due to recent advancements in Myasthenia Gravis treatments over the past decade, including new biological therapies and the impact of the COVID-19 pandemic from 2020 to 2023. Methods We conducted a retrospective analysis of all patients diagnosed with Myasthenia Gravis at our tertiary center between 2000 and 2023, extracting mortality and clinical features compared to two age- and sex-matched control groups of neurological or rheumatologic patients. Results We identified 436 Myasthenic patients and 2,616 controls (1308 in each control group). Myasthenia Gravis mortality was 14% at 5 years (61/422) and 21% at 10 years (87/422). Mortality was significantly higher than control groups (p < 0.001). Intubations during myasthenic crisis were linked to higher mortality (p = 0.002). Bulbar weakness at presentation showed higher mortality but did not reach clinical significance. We compared the mean age at death in MG patients to national life expectancy benchmarks using a one-sample Z-test, revealing significantly younger age at death in both males (78.3 vs. 81.6 years, p = 0.009) and females (76.5 vs. 85.2 years, p < 0.00001). Patients with normal thymic pathology showed better outcomes and lower mortality after thymic removal (p < 0.0001). The primary cause of death was linked to infections, significantly correlated with chronic steroid use. Discussion In conclusion, patients with Myasthenia Gravis had higher mortality rates. Thymic removal reduced mortality, while intubation is associated with increased mortality risk.
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 | 0.001 | 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 | 0.000 |
| 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".