Prediction of the Risk of Bleeding in People Living with Hemophilia
Bibliographic record
Abstract
A tool allowing the prediction of the risk of bleeding in patients with hemophilia would be relevant for patients, stakeholders, and policymakers. We performed a systematic review of the literature searching for available risk assessment models to predict the risk of bleeding in people living with hemophilia, and to determine the key risk factors that the ideal model should include. We also systematically review the literature to determine the acceptability and accuracy of wrist-wearable devices to measure physical activity in the general population. Finally, we validated the performance of a risk assessment model for the prediction of the risk for bleeding in people living with hemophilia. We identified the following risk factors for bleeding in people living with hemophilia: plasma factor levels, history of bleeds, physical activity, antithrombotic treatment, and obesity. The FitBit Charge and FitBit Charge HR are the most accurate devices for measuring steps, and the Apple Watch is the most accurate for measuring heart rate. No device proved to be accurate in measuring energy expenditure. The predictive accuracy of the risk assessment model that we validated does not endorse its use to drive decision making on treatment strategies based on the predicted number of bleeds. This might in part be explained by the methods used in the derivation phase. The need for an accurate risk assessment model to predict the risk of bleeding in people living with hemophilia is still unmet. This should be done by including the relevant risk factors identified through our work, with data on physical activity possibly collected using an accurate wrist-wearable device, and through the application of rigorous methods in the derivation and validation phases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".