New images in haemophilia
Bibliographic record
Abstract
New imaging techniques are valuable for the care of patients with haemophilia. On angiography it is shown that some bleedings in severely damaged joints or after implantation of prostheses are arterial. Effect of clotting factor is often poor. Selective catherization with embolization of the bleeding artery stops the bleed and is clinically effective. From 31 patients with severe haemophilia A or B, 62 knee radiographs were scored according to the Pettersson-scoring system as well as with Knee Digital Image Analysis (KIDA). Using KIDA, good correlation was found for osteoporosis, irregular subchondral surface, narrowing of the joint space, deformity and incongruence. For each of the parameters within one point in the Pettersson score a large variation existed in KIDA grading. MRI is accurate for diagnosis of soft and osteochondral tissue. Nevertheless, it is costly and not very accessible. The use of parallel imaging is more feasible for assessment of multiple joints within a relatively short period of time. Although ultrasonography also holds the potential for being an adjunct to MRI it has the disadvantage that it is operator-dependent. In a cohort of 124 chronically HCV infected haemophilia patients transient elastography was performed to measure liver stiffness. 57 (46%) had no or mild fibrosis, 18 (14.5%) moderate fibrosis and 49 severe or cirrhotic fibrosis. Transient elastography is safe and helpful to refer patients to antiviral therapy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".