Biomarkers for equine joint injury and osteoarthritis
Bibliographic record
Abstract
We report the results of a symposium aimed at identifying validated biomarkers that can be used to complement clinical observations for diagnosis and prognosis of joint injury leading to equine osteoarthritis (OA). Biomarkers might also predict pre-fracture change that could lead to catastrophic bone failure in equine athletes. The workshop was attended by leading scientists in the fields of equine and human musculoskeletal biomarkers to enable cross-disciplinary exchange and improve knowledge in both. Detailed proceedings with strategic planning was written, added to, edited and referenced to develop this manuscript. The most recent information from work in equine and human osteoarthritic biomarkers was accumulated, including the use of personalized healthcare to stratify OA phenotypes, transcriptome analysis of anterior cruciate ligament (ACL) and meniscal injuries in the human knee. The spectrum of "wet" biomarker assays that are antibody based that have achieved usefulness in both humans and horses, imaging biomarkers and the role they can play in equine and human OA was discussed. Prediction of musculoskeletal injury in the horse remains a challenge, and the potential usefulness of spectroscopy, metabolomics, proteomics, and development of biobanks to classify biomarkers in different stages of equine and human OA were reviewed. The participants concluded that new information and studies in equine musculoskeletal biomarkers have potential translational value for humans and vice versa. OA is equally important in humans and horses, and the welfare issues associated with catastrophic musculoskeletal injury in horses add further emphasis to the need for good validated biomarkers in the horse. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:823-831, 2018.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".