In Memoriam Grant Hieshima, MD: 1942–2019: Pioneer, Mentor, Visionary, Friend
Bibliographic record
Abstract
Grant Hieshima, MD died unexpectedly on August 9, 2019, at the age of 77, while enjoying one of his lifelong passions, deep-sea fishing, with his son Michael at his side. Grant was born in Southern California in 1942, and attended UCLA as an undergraduate. He received his medical education from Tulane University Medical School, in New Orleans, where he graduated with honors in 1969. Grant initially wanted to become a general surgeon but subsequently decided to pursue radiology with subspecialty training in neuroradiology and nuclear medicine. He was appointed to a faculty position in 1974 at Harbor-UCLA Medical Center in Torrance, California where he began to develop techniques to manage vascular trauma. Figure 1 Grant Hieshima at the UCSF alumni reunion during the July 2018 SNIS Annual Meeting in San Francisco. In 1983, Dr John Bentson recruited Grant to UCLA Medical Center to start a new program in neurointerventional radiology (NIR). I was completing my final year of residency in radiology at UCLA, and after training with Grant, I asked to become his first NIR fellow. In the 1980s, the field of NIR was just beginning, and pioneers included Dr. Alejandro Berenstein at New York University; Dr. Chuck Kerber at The University of California, San Diego; Dr’s. Fernando Vinuela and Allan Fox at Toronto General Hospital; Dr. Gerard Debrun at the University of Illinois; Dr. Fedor Serbinenko at the Burdenko Neurosurgery Institute in Moscow; Dr’s. Victor Shcheglov and Alexander Zubkov, St Petersburg, Russia; Dr. Pierre Lasjaunias, Hospital Kremlin Bicetre, Paris; Dr. Charlie Strother, University of Wisconsin; and Dr. Grant Hieshima. At UCLA we would start at 7:30 am with morning read outs of CT brain scans, spend the afternoon performing diagnostic angiography, myelography, pneumoenchephalography of the brain ventricles, lumbar punctures for cerebrospinal fluid analysis, and then in the late afternoon would start NIR procedures, usually working until the …
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.050 | 0.031 |
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".