ICON 2019—international scientific tendinopathy symposium: building an ICONic tendon tower—launching a new era in clinical tendinopathy research
Bibliographic record
Abstract
There is a lot we do not know about tendinopathy, but we know many athletes have tendon pain during or after sports. You may be surprised to learn that basic—dare we say foundational—issues surrounding clinical terminology and labels for tendon pathology have never been agreed on. We clinicians and researchers use a very wide range of terms inconsistently. This confuses patients, makes interpreting and comparing new research hard for clinicians and hampers communication among those engaged with the research. Are you looking for a guide on what patient characteristics to report in tendon treatment trials? Good luck! We lack uniform standards for reporting participant characteristics in tendon research. There has been no meeting to discuss the clinical domains that should be included when evaluating patients in tendon trials. Should we measure pain or function do we need imaging? What about quality of life? This Babylonian tower of confusion …
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.021 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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".