PB0621 Emicizumab Plasma Concentrations are not Related to Bleeding Rates Using Standard Dosing, a Call for Dose Adaptation
Bibliographic record
Abstract
Background: Since the publication of the 3rd edition of the World Federation of Hemophilia's (WFH) Guidelines for the Management of Hemophilia in August 2020, the WFH has been implementing a Living Guidelines Model (LGM) to ensure recommendations are kept relevant.Aims: The aim of the WFH LGM is to provide timely updates to the recommendations in the 3rd edition of the WFH Guidelines by updating individual recommendations when new evidence becomes available.Methods: The LGM will be implemented by multi-disciplinary Steering Committee (SC), Guidelines Oversight Committee (GOC) and topic-specific Working Groups (WG).All groups are composed of healthcare professionals, allied healthcare professionals and people with hemophilia/caregivers, with global representation.Individual recommendations will be updated on a prioritization and rotational basis, established by the SC.Systematic reviews of the literature will be conducted with medical librarians, systematic reviewers, methodologists, and guideline and content experts.The WG will update recommendations if there is sufficient new evidence.All updates will be posted on the WFH website and in the Haemophilia Journal.Results: The SC established the first topic area to update will be gene therapy for hemophilia.The Working Group is being assembled and will include 32 members composed of healthcare professionals, allied healthcare professionals and people with hemophilia/caregivers, with the latter group making up 25% of the group.Conflicts of Interest for each member will be reviewed by the GOC prior to approval as a member of the WG.PICOs will be developed in the first quarter of 2023.Conclusion(s): Developing and updating WFH recommendations using the LGM allows for a systematic prioritization of topics and evidence synthesis, with the unit of update as individual recommendations rather than chapters.This results in a more efficient, responsive process reflecting the latest evidence, and leads to updated recommendations earlier than the traditional approach.
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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 | 0.039 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".