Putnam at the New York Neurological Institute: Clinical Activity and Vulnerability
Bibliographic record
Abstract
Abstract There was never anyone as versatile neurologically as Tracy Putnam, not before or since. When he arrived in New York, he had estimable credentials in both neurology and neurosurgery, even also in psychiatry. The closest comparison might have been the great Wilder Penfield, who called himself a “surgical neurologist.” However, Penfield largely restricted his operations to studies of—and treatment for— epilepsy. And he created a great neurological institute in Montreal, where he encouraged Herbert Jasper to join him as electroencephalographer. Together they essentially invented the concept of “temporal lobe epilepsy,” in which the seizures include abnormalities of thought and behavior. These attacks start in areas of the brain that were thought to have been damaged by birth injuries or other trauma. By ingenious surgery, he could excise the seizure focus and relieve the attacks, even though adding more traumatic damage in the process of operating. More than any historical figure in medicine, Penfield showed that the mind is in and of the brain. By stimulating different parts of the brain with an electrical current, he could evoke visions, sounds, and spoken words. Yet, he ended his life as a dualist, believing in a soul separable from the brain. But Penfield did not take on all of neurosurgery himself.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".