CLINICAL SIGNIFICANCE AND PROGNOSIS OF DEEP DIGITAL FLEXOR TENDINOPATHY ASSESSED OVER TIME USING <scp>MRI</scp>
Bibliographic record
Abstract
Deep digital flexor (DDF) tendinopathy is one of the most frequent causes of foot lameness and the prognosis is guarded. The progress of lesion healing may be followed by magnetic resonance (MR) imaging to formulate a prognosis and to adapt the rehabilitation program. We assessed the correlation of outcome with total tendon damage and temporal resolution of MR abnormalities. Images from 34 horses with DDF tendinopathy that had undergone at least two low-field standing MR examinations of the foot (mean 2.5 ± 1.3 times) were reviewed. No horse having a T1-GRE hyperintense lesion over 30 mm in length or over 10% tendon cross-sectional area returned to its previous activity level. Horses with concomitant lesions had worse outcome than horses with DDF tendinopathy only (P = 0.005). In all horses including those with excellent outcome, the lesion persisted, even mildly, in T1-GRE and PD images. Horses with tendon lesion resolution on STIR-FSE and T2-FSE images on recheck examination had a better outcome (P = 0.0004 and P = 0.002, respectively), and all horses that returned to their previous level of performance had complete resolution of signal hyperintensity on the STIR-FSE sequence. Although rehabilitation remains multifactorial, characteristics of DDF tendinopathy and concomitant lesions on first and recheck MR examinations allow refining the prognosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".