Point of care ultrasound in haemophilia: Building a strong foundation for clinical implementation
Bibliographic record
Abstract
.Haemophilia A and B are X-linked recessive diseases that result in a deficiency in coagulation factor VIII and IX respectively. These low levels of factor cause a decrease in thrombin formation, which in turn promotes haemorrhage into articular joints.1 The resultant blood products stimulate chondrocyte apoptosis, synovitis and subchondral bone changes, leading to arthropathy.2 Even a single episode of haemarthrosis can potentially increase the risk of poor long-term joint outcomes.3, 4 Accurate diagnosis and timely therapy of joint haemorrhage are then critical to patient care. Investigators utilize physical assessment for diagnosis; however, recent studies have shown that the physical exam alone lacks accuracy in determining the extent of haemarthrosis and its resolution.3, 5, 6 As a result, patients/providers may prematurely lower their factor treatment doses and return to normal physical activity, which may lead to re-bleeding. Several imaging modalities are used in the diagnosis of haemarthrosis. Ultrasound (US), when compared to magnetic resonance imaging (MRI) and computed tomography (CT), is quick, non-ionizing, inexpensive and accurate in diagnosing soft tissue abnormalities in patients with haemophilia.3, 7 Point-of-care-ultrasound (POC-US) has recently gained recognition in its use as an adjunct tool in the haemophilia setting.6, 8 However, the user dependency of POC-US necessitates that users be properly trained and competent in the technology to avoid misuse and misdiagnosis. Guidelines and recommendations should be established to facilitate appropriate training, use, and implementation of POC-US as a diagnostic tool in haemophilia.
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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 | 0.114 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".