Evaluation of Point‐of‐Care Ultrasound in Paediatric Haemophilic Arthropathy: A Prospective Comparative Study With Comprehensive Ultrasound and MRI
Bibliographic record
Abstract
AIM: To evaluate the diagnostic accuracy of point-of-care (POC) ultrasound (US) in detecting joint changes in haemophilic arthropathy compared to comprehensive US, physical examination and MRI. METHODS: Single-centre, prospective study examining 22 ankles, eight knees and nine elbows of 28 subjects (median/range, 16/6-27 years) with inherited bleeding disorders. Same-day physical and imaging joint assessment was performed using Haemophilia Joint Health Score (HJHS) 2.1, POC-US, comprehensive US and MRI. Two readers reviewed the imaging studies using standardized scoring systems. The inter-reader agreement was assessed with weighted kappa statistics. Diagnostic test performance of POC-US and comprehensive US was benchmarked against MRI findings using sensitivity, specificity, predictive values and accuracy. Spearman correlation was used to correlate physical examination and imaging findings. RESULTS: Moderate inter-reader agreement was observed for soft-tissue changes (kappa = 0.58), which was lower than comprehensive US (kappa = 0.76) and MRI (kappa = 0.77). Agreement for osteochondral lesions was poor (κ 0.09), trailing behind comprehensive US (kappa = 0.55) and MRI (kappa = 0.75). POC-US showed reduced accuracy across all joint parameters in the elbows and ankles compared to comprehensive US, but comparable diagnostic performance in the knees. A good correlation was noted between total POC-US scores and HJHS (r = 0.68, p < 0.0001), similar to comprehensive US (r = 0.66, p < 0.0001). CONCLUSION: POC-US is valuable for assessing soft-tissue changes in haemophilic arthropathy, correlates satisfactorily with physical examination, but its accuracy is inferior to comprehensive US for ankles and elbows. Both POC-US and comprehensive US should be employed cautiously for assessing osteochondral joint abnormalities.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".