Bibliographic record
Abstract
Abstract Osler’s first private patient in Baltimore was an important elderly gentleman, a Hopkins trustee perhaps. Osler thought he felt a pelvic tumor, diagnosed it as an inoperable sarcoma, ‘and in as gentle a way as I could, told his wife, and advised that a surgeon see the case.’ The surgeon came the next day, drained the patient’s distended bladder with a catheter, and thus disposed of the ‘tumor.’ Osler used the embarrassment as an object lesson in his teaching. Otherwise, his private practice flourished. Consultations grew steadily in Baltimore until by the mid-90s he was seeing all the private patients he could handle and had to turn down an increasing number of requests (including, it is said, a man who then came back and read the Hippocratic Oath on his doorstep). He was consulting physician to the huge extended Osler family, to all his old friends back in Canada, to the students, nurses, doctors and their relatives at Hopkins, to the elite of Baltimore, to congressmen and several denizens of the White House, and to cases that interested him up and down the eastern seaboard and as far west as Wisconsin and Iowa.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.174 | 0.061 |
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".