THE HISTORY OF NEUROSURGERY AT THE HOSPITAL FOR SICK CHILDREN IN TORONTO
Bibliographic record
Abstract
The tradition of selfless charity for children in Toronto was established by Elizabeth McMaster, founder of the Hospital for Sick Children, or "Sick Kids," in 1875. The tradition of fortitude and perseverance in trying to cure children who were deemed incurable by others was sealed into the history of the Hospital by early pioneering surgeons, including Drs. Clarence Starr, A.W. Farmer, and William Gallie. Dr. William Gallie recognized the importance of neurosurgery for the future of the Hospital and encouraged Dr. William Keith to acquire training in pediatric neurosurgery in Chicago and London. Dr. Keith began the practice of pediatric neurosurgery at Sick Kids in 1935 and worked in the primordial phase of the subspecialty for the next 20 years until he was joined by Dr. E. Bruce Hendrick in 1955. In time, Dr. Hendrick was joined by Drs. Harold Hoffman and Robin Humphreys who led a decidedly strong pediatric neurosurgery unit between 1975 and 1995. During this epoch, the pediatric neurosurgery service grew to become one of the busiest and most progressive units in the world. Over the years, numerous neurosurgery fellows and faculty from all over the world have trained at or have visited Sick Kids to learn how pediatric neurosurgery is practiced in Toronto. The purpose of this article is to review the history of the individuals who founded the Hospital for Sick Children and its Division of Neurosurgery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".