Bibliographic record
Abstract
Largely because of the advances of the Civil Rights movement in the mid-20th century, an increasing number of African-Americans have had the opportunity to become physicians and enter the distinguished field of neurosurgery. Many have made the most of this opportunity, becoming prominent in both academics and private practice. Unfortunately, the details regarding the first African-American neurosurgeon, Clarence Sumner Greene, Sr., have remained in relative obscurity. Born on December 26, 1901 in Washington, D.C., Dr. Greene received his M.D. from the Howard University College of Medicine with distinction in 1936. After 7 years of general surgery residency and 4 years as a professor of surgery at Howard University, he was granted the opportunity by the legendary Wilder G. Penfield to train in neurosurgery at the world-renowned Montreal Neurological Institute from 1947 to 1949. Receiving high praise from Dr. Penfield, Dr. Greene became the first African-American certified by the American Board of Neurological Surgery on October 22, 1953. Subsequently, he was appointed as chair of neurosurgery at Howard University, where he successfully treated intracranial aneurysms, brain tumors, and herniated intervertebral discs until his tragic death in 1957. The diligence and perseverance of Clarence Sumner Greene, Sr., M.D., D.D.S., F.A.C.S. enabled him to overcome incredible odds to become the first African-American neurosurgeon, trained by Dr. Penfield at the Montreal Neurological Institute. A true pioneer, his achievements have opened the door for subsequent African-Americans to enhance the field 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.158 | 0.092 |
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".