From shame to fame: The improbable career of the great anatomist‐surgeon D. Hayes Agnew
Bibliographic record
Abstract
David Hayes Agnew began his career as a country doctor in rural Pennsylvania in 1838. After a 3-year diversion as a partner in a large ironworks business that went bankrupt in 1846, Agnew, seriously in debt, returned to the medical profession in Cochranville, PA, hoping to focus on surgery. Recognizing that he needed to improve his knowledge of anatomy, he purchased cadavers from Philadelphia and dissected at home in his spare time. When he was finished dissecting, he covertly moved bodies to a nearby pond so he could later collect skeletons after eels had removed the remaining soft tissues. This worked well until Agnew and the townspeople found out that a local fisherman, known for selling the most delicious eels, fished exclusively from this pond. Agnew was asked to leave Cochranville and he moved to Philadelphia, where he purchased the Philadelphia School of Anatomy and worked as a surgeon at Blockley Hospital. During the Civil War, he became renowned for his ability to manage gunshot wounds. Agnew was upwardly mobile in Philadelphia, becoming one of America's most prominent 19th-century academic surgeons. When President James Garfield was shot by an assassin, Agnew was called to care for him. When he retired from his position as the John Rhea Barton Professor of the Principles and Practice of Surgery at the University of Pennsylvania, the medical students hired the famous American realist painter, Thomas Eakins, to produce The Agnew Clinic, which became one of the artist's two most important paintings. Clin. Anat. 32:661-671, 2019. © 2019 Wiley Periodicals, Inc.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".