State‐of‐the‐art imaging techniques for the evaluation of haemophilic arthropathy: present and future
Bibliographic record
Abstract
SUMMARY: In spite of the fact that the diagnosis of haemophilia is essentially clinical and laboratory-based, imaging has become an important tool for the evaluation of complications, diagnostic confirmation and/or complementation and therapeutic follow-up in haemophilic arthropathy. Radiography remains the workforce horse in the diagnosis and follow-up of haemophilic arthropathy. The radiographical findings in arthropathy follow an expected sequence of events and are overall similar in different joints. Magnetic resonance imaging (MRI) has advantages over radiography based on its capability of visualizing soft tissue and cartilage changes in haemophilic joints. The recent development and standardization of MRI scoring systems for measuring soft tissue and cartilage abnormalities may enable the comparison of pathological joint findings in clinical trials conducted at different institutions across the world. The implementation of high-frequency transducers and colour/power Doppler capabilities has provided new insights for clinical applications of ultrasonography (US) in haemophilic arthropathy. In spite of the imaging modality's technical challenges such as operator-dependency, US has advantages over MRI. One of these advantages is its ability of differentiating synovium hypertrophy and hemosiderin deposition, which is not possible with MRI given the presence of susceptibility artefacts from extracellular hemosiderin on gradient-echo MR images. In addition to the aforementioned conventional imaging modalities, novel imaging techniques (blood oxygen level dependent, ultrasmall superparamagnetic iron-oxide contrast-enhanced, and T1 and T2 mapping MRI, ultrasound biomicroscopy, microbubble contrast-enhanced US and positron emission tomography, among others) hold promise for early assessment of haemophilic arthropathy in the future upon completion of their clinical validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".