Clinical Reasoning: A complicated case of MELAS
Bibliographic record
Abstract
A 48-year-old right-hand-dominant woman presented to the emergency department with sudden onset of visual loss of her left visual field. She had a complicated medical history—end-stage renal disease requiring renal transplant 6 years prior to presentation secondary to biopsy-proven focal segmental glomerulosclerosis (FSGS), coronary artery disease, left atrial enlargement due to pulmonic stenosis, progressive sensorineural hearing loss 10 years prior and subsequent placement of a right cochlear implant, hypertension, and type 2 diabetes mellitus for the last year. At presentation, the patient's medications included cotrimoxazole prophylaxis, mycophenolate mofetil, tacrolimus, insulin, and folic acid. On examination, she was of short stature and had a low-grade fever and a left homonymous hemianopsia (L-HH). Within the next 3 weeks, she subsequently developed further visual disturbance to the point of being cortically blind in addition to new left upper extremity paresthesias. Initial CT head scan showed a subacute right occipital infarct and bilateral basal ganglia calcification. Subsequent CT imaging done 3 weeks later demonstrated interval development of new left temporoparietal and left occipital infarcts (figure, A). CT angiogram showed intracranial stenoses affecting basilar artery, both posterior cerebral arteries, and to a lesser degree bilateral anterior cerebral arteries and middle cerebral arteries, suggestive of possible atherosclerosis, but were not believed to be hemodynamically significant (figure, B). Due to her cochlear implant, significant streak artifact affected the interpretation of her CT scans and precluded her from obtaining an MRI.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| 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".