Research Progress in the Last Quarter of the 20th Century at the University of Tokyo and Tokyo Women’s Medical University
Bibliographic record
Abstract
Professor Keiji Sano described the history of neurosurgery in Japan until 1975. After World War II, not only neurosurgery but all fields of medicine were devastated in Japan. Professor Sano contributed greatly to the reform and modernization of neurosurgery during that very difficult era in Japan. He performed much research by himself and also as a leader of research groups on stereotactic and functional neurosurgery, cerebrovascular diseases, head injuries, and brain tumors. He organized the Fifth International Congress of Neurological Surgery in Tokyo in 1973. I succeeded in the chairmanship of the Department of Neurosurgery of the University of Tokyo in 1981. We have performed research on the treatment of brain tumors and cerebrovascular diseases. To obtain the best results for brain tumor treatment, we have introduced several new radiotherapeutic methods, such as the gamma knife, heavy-particle irradiation, and the photon radiosurgery system. To improve surgical treatment, we have energetically engaged in medical engineering research on computer-assisted surgical systems (intraoperative monitoring and navigation systems). We have also performed much research on chemotherapy and immunotherapy. In the field of cerebrovascular diseases, the main research projects have been focused on the mechanism and treatment of vasospasm and brain edema after subarachnoid hemorrhage. I summarize the results of our research performed in the Department of Neurosurgery of the University of Tokyo until 1992 and at Tokyo Women's Medical University after 1992, in the last quarter of the 20th century.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".