Patterns of joint damage in severe haemophilia A treated with prophylaxis
Bibliographic record
Abstract
OBJECTIVE: The primary objective of this study was to assess whether there are different patterns (classes) of joint health in young boys with severe haemophilia A (SHA) prescribed primary tailored prophylaxis. We also assessed whether age at first index joint bleed, blood group, FVIII gene abnormality variant, factor VIII trough level, first-year bleeding rate and adherence to the prescribed prophylaxis regimen significantly predicted joint damage trajectory, and thus class membership. METHODS: Using data collected prospectively as part of the Canadian Hemophilia Primary Prophylaxis Study (CHPS), we implemented a latent class growth mixture model technique to determine how many joint damage classes existed within the cohort. We used a multinomial logistic regression to predict the odds of class membership based on the above predictors. We fitted a survival model to assess whether there were differences in the rate of dose escalation across the groups. RESULTS: We identified three distinct classes of trajectory: persistently low, moderately increasing and rapidly increasing joint scores. By multinomial regression, we found that only age at first index joint bleed predicted rapidly increasing joint scores. The rapidly increasing joint score class group moved through dose escalation significantly faster than the other two groups. CONCLUSIONS: Using tailored prophylaxis, boys with SHA follow one of three joint health trajectories. By using knowledge of disease trajectories, clinicians may be able to adjust treatment according to a subject's predicted long-term joint health and institute cost-effective programmes of prophylaxis targeted at the individual subject level.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".