Bibliographic record
Abstract
The annual incidence of Miller Fisher syndrome is about 0.1 per 100 000 population in the UK.1 According to the World Health Organization, there are about 20 000 neurologists in Western Europe, the USA and Canada. Two of them will have the Miller Fisher syndrome in this century. I was one of those two and would like to report my own case, review the syndrome and communicate my personal experience with the disease. At the age of 51 years, I woke up with double vision due to a left trochlear palsy 10 days following the beginning of a heavy cough with fever. My tendon reflexes were decreased on the left. Neck and brain arteries as well as brainstem imaging were all normal. I thought I had a parainfectious trochlear palsy and attributed the depressed tendon reflexes to my tension during the examination by one of my colleagues. However, the next morning I had trouble standing up and instantly thought that I might have the Miller Fisher syndrome, but was sceptical because it is so rare. I was admitted to hospital where I had bilateral mild ptosis, dilated pupils not reacting to light and opthalmoplegia sparing only a little adduction and down gaze. Deep tendon reflexes were by then largely absent, peripheral motor functions were normal and I had slight limb and moderate truncal and gait ataxia. Sensory functions were unaffected except for numbness of my fingertips. CSF protein concentration was …
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.200 | 0.060 |
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".