Patient‐derived assessment tool using musculoskeletal ultrasound for validation of haemarthrosis
Bibliographic record
Abstract
INTRODUCTION: Haemophilia patients experience painful joint episodes which may or may not be associated with haemarthrosis. We sought to validate a questionnaire developed by the Canadian Haemophilia Society using point-of-care musculoskeletal ultrasound (POC MSKUS) to confirm haemarthrosis. METHODS: The questionnaire comprised of 20 questions (10 each associated with haemarthrosis and arthritis pain) and was administered to adult haemophilia patients reporting to the Haemophilia Treatment Centre (University of California San Diego). We confirmed the presence (or absence) of haemarthrosis using POC MSKUS [Joint Activity and Damage Exam (JADE)]. We fitted univariate and multivariate generalized estimating equations to identify symptoms associated with haemarthrosis. RESULTS: We evaluated 79 painful episodes in 32 patients [median age = 38 years (range 21-74)]. POC MSKUS detected haemarthrosis in 36 (46%) episodes. The strongest predictor for haemarthrosis pain was 'like a balloon swelling with water' (odds ratio [OR] 2.88 [CI .68;12.10]); 'no feeling of sponginess with movement' (OR .24[CI .07;.76]) was the strongest for arthritic pain. We identified four questions with the strongest OR for differentiating haemarthrosis pain from arthritic pain to develop an algorithm for haemarthrosis prediction. Answering these questions in "yes/no" fashion yielded estimates of the probability of haemarthrosis CONCLUSION: Objective diagnosis of haemarthrosis by MSKUS facilitated the development of a symptom-based prediction tool for diagnosis of haemarthrosis. The tool requires further validation and will be particularly helpful in situations where MSKUS is not readily available.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".