Remembering Our Roots
Bibliographic record
Abstract
For as long as athletes have been competing, injuries from competition have resulted. Sports medicine has a rich and storied history with significant contributors from many different countries and civilizations. Over time, we have honored the contributions of important figures in sports medicine with the use of eponyms. However, the continued use of eponyms in medicine has been called into question by a number of authors. They cite inaccuracies in definition and context, lack of descriptive value, and the possible celebration of unsavory characters. However, eponyms are pervasive in the medical literature. They bring color and character and allow us to honor those who came before us. Furthermore, eponyms can hide some distressing aspects of a disease. This review of eponyms in sports medicine provides an opportunity to celebrate our predecessors, recognize the international flavor of sports medicine, and promote accurate use of eponyms for the future.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.036 | 0.023 |
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".