History of neurosurgery at Howard University: the nation’s only historically black academic institution that practices neurological surgery
Bibliographic record
Abstract
Howard University Hospital has been a pillar for healthcare delivery in the Black community, an underserved sector of Washington, DC, since its founding in 1862. Neurological surgery, one of the many areas of service provided, was established by trailblazer Dr. Clarence Greene Sr., who was appointed the division's first chief in 1949. Because of the color of his skin, Dr. Greene had to complete his neurosurgical training at the Montreal Neurological Institute, as he was refused the opportunity to train in the United States. He went on to become the first African American to be board certified in neurological surgery in 1953. Drs. Jesse Barber, Gary Dennis, and Damirez Fossett, the subsequent division chiefs, have all continued Dr. Greene's legacy of providing academic enrichment and subserving a disparate population. Many patients who may not have received treatment otherwise have been able to receive exemplary neurosurgical care from them. Under their tutelage, numerous African American medical students have gone on to train in neurological surgery. Future directions include developing a residency program, collaborating with other neurosurgery programs in continental Africa and the Caribbean, and establishing a fellowship for training international students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".