Evaluation of Standard and Proposed Reference Values for Entheseal Thickening by Using Musculoskeletal Ultrasound
Bibliographic record
Abstract
OBJECTIVE: Ultrasound (US) is increasingly used to evaluate enthesitis. One of the US features of enthesitis is thickening. However, there is no consensus on how the entheseal thickening needs to be defined, and existing cut-off levels have been criticized for being frequently positive in healthy controls (HCs). Our objective was to determine the frequency of thickening of entheses on US using the existing cut-off values in HCs and in patients with axial spondyloarthritis (axSpA) and propose new values to improve discriminative value. METHODS: Eighty HCs and 100 patients with axSpA had US scans of 2160 entheses. Sensitivity, specificity, odds ratio (OR), and accuracy were calculated according to accepted cut-off levels from the literature and proposed cut-offs were calculated as the mean ± 2 SD. RESULTS: Thickening according to current cut-off levels was found in 20.4% (196/960) of healthy participants' entheses and 33% (396/1200) of entheses of patients with axSpA. Thickening according to proposed cut-off levels decreased frequency of thickening in both groups, and therefore increased specificity at the cost of decreasing sensitivity. The only anatomical site where the thickness had a value to discriminate disease from health was seen at the triceps tendon enthesis with an OR of 13.4 (95% CI 4.0-44.8) according to the current cut-offs compared to 10.3 (95% CI 4.0-26.6) with the proposed cut-off levels. CONCLUSION: Although using cut-offs appears to be an appealing method to evaluate entheseal thickness, the measurements may be affected by several confounding factors, leading to a low discriminative value, except for at the triceps tendon enthesis.
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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 | 0.042 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".