Penfield and the National Institute of Neurological Diseases and Blindness: A Unique Training and Institutional Model Coupled with Mentorship
Bibliographic record
Abstract
Richard Leblanc's article 1 traces the history of the establishment of the medical and surgical neurology branches of the National Institute of Neurological Diseases and Blindness (NINDB).The story of this successful endeavor is really a reflection of Wilder Penfield's original professional goals.2,3 The vision for this structure can be traced to academic principles laid out in a 1917 McGill University proposal, while the operational history of a neurosurgical department and the beginnings of the Montreal Neurological Institute (MNI) owe to the efforts of famed thoracic surgeon Edward Archibald, who enticed Penfield to move to Montreal.3 After World War II, there was a clear need for improved neurological and neurosurgical training in North America and Europe, which provided the impetus for the idea of the NINDB branches.Highly motivated and expert medical personnel with military service played a central role within the US Veterans Administration in rebuilding American neurology after the war.Although various well-known figures in neurosurgery and neurology had formed departments at prominent university medical centers in the United States and Canada, there was a dearth of structured neurological and neurosurgical training that also incorporated formal scientific programs.4 Leblanc described the first decade of training and research performed by neurosurgical residents under Penfield at the MNI, which had a structure that allowed residents to train in neurosurgery, neurology, and basic science under one roof.5,6 The training basis begun by Penfield and William Cone in 1928, developed through the founding of the MNI in 1933 and its opening in 1934, was a recipe for producing many of the world's greatest neurologists, neurosurgeons, and scientists for decades thereafter.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 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".