A utopian idea: Cushing, Bailey, Penfield, and the National Institute of Neurological Diseases and Blindness
Bibliographic record
Abstract
Toward the end of the First World War, Harvey Cushing conceived of a National Institute of Neurology (NIN) that would integrate neurology, neurosurgery, psychiatry, and allied disciplines within a single institution. It would first be established for the care of American casualties in an existing military hospital in France, and then relocate to the United States. Cushing was unsuccessful in acquiring funding for this project despite appeals to the army and to the Carnegie and Rockefeller foundations. By 1920 the idea had faded from memory. In 1933 Wilder Penfield was successful in obtaining funding from the Rockefeller Foundation for the creation of the Montreal Neurological Institute (MNI). The MNI's faculty held full-time university appointments and they limited their practice to the institute, where their offices and clinics were housed, and to adjoining research laboratories in neuroanatomy, neurochemistry, neurophysiology, and neuropsychology, as Cushing had envisioned. In this paper the argument is made that although Cushing's plan for the NIN was premature, the success of the MNI proved its feasibility. In addition, the MNI's success in integrating clinical care and research within a single institution was a model for the National Institute of Neurological Diseases and Blindness and drove its first clinical research program.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.024 |
| 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".