Bibliographic record
Abstract
The advances of the Civil Rights movement in the mid-20th century and the success of the first African-American neurosurgeons trained at the Montreal Neurological Institute have led to a number of African-Americans receiving neurosurgery training within the United States. Unfortunately, the details regarding the first African-American neurosurgeon trained in the United States, E. Latunde Odeku, have largely remained in obscurity. Born on June 29, 1927 in Lagos, Nigeria, Dr. Odeku received his M.D. from the Howard University College of Medicine in 1954. He spent the next year at the University of Michigan under the tutelage of Edgar A. Kahn, chief of neurosurgery, and was impressive enough to be offered a residency position. From 1956 to 1960, he trained under Dr. Kahn at the University of Michigan. In 1961, he returned to Howard as a member of the neurosurgery faculty, during which time he became the second African-American to be certified by the American Board of Neurological Surgery. Although he received multiple job offers in the United States, he chose to return to Nigeria where he worked tirelessly, providing excellent neurosurgical care and discipleship until his death in 1974. The diligence and intelligence of E. Latunde Odeku, M.D., F.A.C.S., enabled him to become the first African-American neurosurgeon trained in the United States. A truly global pioneer, his selfless service in America and Nigeria opened the door for people from each country 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.286 | 0.115 |
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".