Low probability of myasthenia Gravis in patients presenting to neuro-ophthalmology clinic for evaluation of isolated ptosis
Bibliographic record
Abstract
BACKGROUND: Concerning causes of ptosis, most notably third nerve palsy and Horner's syndrome, can be ruled out with normal ocular motility and pupillary examination. Myasthenia gravis (MG) however, rarely can present with ptosis as an isolated finding. We reviewed all patients presenting to tertiary neuro-ophthalmology practice with ptosis of unknown etiology to determine the frequency of MG. METHODS: Retrospective chart review of patients referred to a tertiary neuro-ophthalmology practice with undifferentiated ptosis. RESULTS: Sixty patients were included in the study. Twenty eight (47%) patients had ptosis along with various abnormalities of ocular motility and/or alignment and 32 (53%) had isolated unilateral ptosis defined as ptosis with absence of diplopia, or symptoms of generalized MG (GMG). Final diagnosis was aponeurotic ptosis due to levator palpebrae dehiscence in the majority (73%) of patients, while 10 (17%) were diagnosed with MG (6 with OMG, 4 with GMG). Diplopia was present in 9/10 patients with MG and 8/10 had abnormal ocular findings on clinical examination such as orbicularis oculi weakness, Cogan's lid twitch or fatiguability of ptosis on sustained upgaze. Only one patient referred for isolated unilateral ptosis was diagnosed with OMG and this patient had orbicularis oculi weakness. CONCLUSIONS: None of the patients with isolated unilateral ptosis and otherwise normal examination had MG. All patients eventually diagnosed with MG had diplopia or orbicularis weakness on examination. Thus, the yield of investigating patients with isolated ptosis for MG is exceedingly low.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".