From da Vinci to definitive diagnosis: how training in sports ultrasound harnesses sound, science and skill
Bibliographic record
Abstract
Viennese neurologist Dr Karl Dussik’s 1942 paper Uber die Moglichkeit hochfrequente mechanische schwingingen als diagnosticsches hilfsmittel zu verwerten (On the possibility of using ultrasound waves as a diagnostic aid)1 could be considered the precursor of modern ultrasound curricula, building on scientific findings that began with Leonardo da Vinci, who first recorded experiments listening to sound transmitted through water by placing a tube into the sea to evaluate what he could hear as early as 1490. Medical use of ultrasound was popularised in gynaecology and obstetrics from the 1950s. Other fields such as cardiology, surgery, urology and vascular medicine followed in channelling the diagnostic power of ultrasound technology.2 Musculoskeletal medicine and the application of ultrasound in sports settings had a relatively late start. In 1996, Gibbon published the first textbook on the topic, “Musculoskeletal Ultrasound: The Essentials” describing a “mini-atlas of the normal and abnormal sonographic appearances of the adult joints”.3 The reach of musculoskeletal ultrasound into Sports and Exercise Medicine (SEM) practice and curricula is disparate, varying in several aspects including quality, regulation and accessibility. Such discrepancies may be further exaggerated by varying image resolution concomitant with equipment features and cost. In the UK, ultrasound training is not a mandatory part of the SEM curriculum but is seen as an additional sub-speciality skill. It is usually achieved via an instructional course and then a written and practical assessment, leading to a Certificate of Competence. The …
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".