Levator Palpebrae Biopsy and Diagnosis of Progressive External Ophthalmoplegia
Bibliographic record
Abstract
BACKGROUND: Progressive external ophthalmoplegia (PEO) is a mitochondrial myopathy of ocular muscles. Diagnostic investigation usually involves limb skeletal muscle biopsy and molecular genetic studies, although diagnostic yield tends to be low. The purpose of this study was to evaluate the diagnostic yield obtained by analysis of levator palpebrae (LP) muscle tissue. METHODS: This is a clinicopathologic study of 8 patients with a diagnosis of PEO, who had LP muscle biopsies as part of oculoplastic procedures. Six of these patients also had limb muscle biopsies. Histopathology, electron microscopy and genetic studies were performed. RESULTS: Diagnostic histopathologic findings were present in 4/6 quadriceps biopsies, and 7/8 LP biopsies. Genetic testing on DNA extracted from LP muscle revealed abnormalities in 4 patients. CONCLUSION: In patients whose LP. muscle demonstrate both genetic defects and histopathological abnormalities, the diagnosis of PEO can be confirmed without limb muscle biopsy. Patients having LP resection during oculoplastics procedures for treatment of ptosis may therefore be able to avoid a separate procedure for limb muscle biopsy. Further study is required to determine the specificity of these findings.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".