People behind Exclusive Eponyms of Radiologic Signs (Part II)
Bibliographic record
Abstract
We continue with an article that describes the people behind eponyms in radiology. The collection of the biographical details about these people took us on a fascinating search in immigration archives and into contact with family friends and descendants of these people. This search helped to find some previously unpublished data and photographs, which made a fascinating tour to the past exciting and fruitful. We discovered that eponyms sometimes emerged as a result of a single article, which was not necessarily a significant step in the author’s career. Only a few eponyms are used in radiologic practice, unlike in the specialties of neurology or surgery. Lewicki suggested that this fact as well as the end of the eponym era a few decades ago probably paralleled other changes in medicine, with the discipline becoming more scientific and less descriptive. However, eponyms help us to remember that, even today, when our lives are so dominated by technology, advancement of knowledge still depends on people. As mentioned in the first part, we were dedicated to the names behind the exclusive eponyms of radiologic signs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".