Bibliographic record
Abstract
As the field of performing arts medicine continues to advance, it is essential that we maintain the trust that has been built over the last quarter century with the dancers, musicians, and other performing artists we serve. Trust is a precious commodity that is built over time, largely between individual health care professionals and the patients for whom they care. However, other things we do (or don't do) can have a major influence on the trust and confidence that others place in us. One of these is research and the way we conduct research, especially when it involves human subjects. The public's confidence in medical researchers has been shaken in the last few years as the result of a few well-publicized “bad outcomes” in clinical studies being done at leading academic medical centers in the U.S. and elsewhere. While we are unlikely to do gene-transfer or new drug development studies in an effort to address the health problems of musicians and dancers, we should still hold ourselves to the same ethical standards that apply to the rest of the healthcare world.
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 | 0.252 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.111 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.032 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 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".