Bibliographic record
Abstract
C. Miller Fisher MD, one of the great neurologists in the 20th century, died in April 2012. Born in Canada, he studied medicine at the University of Toronto. As a Canadian Navy medical doctor he participated in World War II and was a war prisoner from 1941 to 1944. He did a residency in neurology at the Montreal Neurological Institute between 1946 and 1948, and later on was a Fellow in Neurology and Neuropathology at the Boston City Hospital. In 1954 he entered the Massachusetts General Hospital as a neurologist and neuropathologist, where he remained until his retirement, in 2005. His academic career ended as Professor Emeritus at Harvard University. His area of special interest in neurology was cerebrovascular disease (CVD). In 1954 he created the first Vascular Neurology service in the world and trained many leading neurologists on this field. His scientific contributions are present in more than 250 publications, as journal articles and book chapters. Many of his articles, certainly not restricted to CVD, were seminal in neurology. Several concepts and terms that he coined are currently used in daily clinical practice. The chapters on CVD, in seven consecutive editions of Harrison's Internal Medicine textbook, are among his highlights. His death was deeply felt by the neurological community.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".