OMICS and 21st Century Brain Surgery from Education to Practice: James Rutka of the University of Toronto Interviewed by Joseph B. Martin (Boston) and Türker Kılıç (İstanbul)
Bibliographic record
Abstract
The Science-in-Backstage interviews aim to share experiences by global medical and life sciences thought leaders on emergent technologies and novel scientific, medical, and educational practices, situating them in both a historical and contemporary science context so as to "look into the biotechnology and innovation futures" reflexively and intelligently. OMICS systems diagnostics and personalized medicine are greatly impacting brain surgery, not to forget the training of the next generation of neurosurgeons. What do the futures hold for the practice of, and education in 21(st) century brain surgery in the age of OMICS systems science, personalized medicine, and the use of simulation in surgeon training? James Rutka is a clinician scientist and a world leader in diagnosis and treatment of brain tumors. He is Professor and Chair of the Department of Surgery at the Faculty of Medicine, University of Toronto, a President Emeritus of the American Association of Neurological Surgeons, and Editor-in-Chief of the Journal of Neurosurgery. Professor Rutka was interviewed for the global medical, biotechnology, and life sciences readership of the OMICS: A Journal of Integrative Biology to speak on these pressing questions in his personal capacity as an independent senior scholar. The issues debated in the present interview are of broad relevance for 21(st) century surgery and postgenomics medicine. The interviewers were Professor Joseph B. Martin, Harvard Medical School Dean Emeritus in Boston and Joint Dean of Medicine at Bahçeşehir University in İstanbul, and the author of "Alfalfa to Ivy: Memoir of a Harvard Medical School Dean," and Professor Türker Kılıç, Dean of Medicine at Bahçeşehir University in İstanbul, and an elected member of the Turkish Academy of Sciences.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.029 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.017 |
| 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".