Bibliographic record
Abstract
Abstract: When Charles Miller Fisher was born in 1913, there was little scientific knowledge about brain diseases and their treatment. Views of stroke, one of the most common and most feared among brain conditions, almost completely flip-flopped during the 20th century. At the midpoint of the century, when Fisher began his career, there was little public or medical interest in stroke. By the end of the century, stroke care and research were among the most intensely active areas within all of medicine. This book is the story of that change and of one physician, Dr. C. Miller Fisher, a main architect and driver of that change. Fisher’s university and medical training occurred in Canada. After a medical internship, he enlisted in the Canadian Navy, early during World War II. After his ship was sunk, he spent 3½ years in a prisoner-of-war camp in Germany. He became interested in stroke during postdoctoral studies in Boston. During a half-century career in Montreal and at Massachusetts General Hospital in Boston, he devoted his career to stroke. Much of the change in the care of patients with stroke and cerebrovascular disease can be directly attributable to his research, his writings, and his teachings and to the physicians he mentored lovingly during his long and fruitful career.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.111 | 0.048 |
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".