P.164 Analysis of MNI OR log book over Dr Wilder Penfield`s Career: Practice profile of epilepsy cases
Bibliographic record
Abstract
Background: Dr. Penfield was a pioneer in neurosurgery and his contribution continues to impact the practice today. Our objective is to analyze the epilepsy surgeries during his career. Methods: Analysis of original operating room books from 1934-1960. Results: He performed 2338 procedures during his career. 601 (26%) epilepsy, 524 (22%) oncology, 441 (19%) general neurosurgery, 379 (16%) functional, 230 (10%) spine, 80 (3%) trauma, 54 (2%) vascular, 29 (1%) nerves. Epilepsy cases were divided: local vs. general anesthesia and a focal resection or lobectomy. From 1934-1945 he performed 167 procedures, 146 local anesthesia with focal resection. From 1946-1950 223 procedures, introduction of lobectomies with 30. Most right-side procedures under general anesthesia. From 1951-1955 152 procedures, 88 focal resection, 64 lobectomies. From 1956-1960 59 surgeries, similar number of focal resection and lobectomies. Conclusions: To our knowledge this is the most complete and comprehensive account his surgical career. In early years patients were treated through large craniotomies with EEG stimulation to tailor focal resections now known as the “Montreal procedure”. This led to a better understanding of human cortex and the division of the brain functions. During later years, there was a reduction in the number of cases done under local anesthesia and increase in lobectomies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".