Methodologies for data collection in congenital haemophilia with inhibitors (<scp>CH</scp>wI): critical assessment of the literature and lessons learned from recombinant factor <scp>VII</scp>a
Bibliographic record
Abstract
AIMS: To systematically review the effectiveness of on-demand treatment with recombinant coagulation factor VIIa (rFVIIa) in congenital haemophilia with inhibitors and, if feasible, perform a meta-analysis of the data. MATERIALS AND METHODS: In-Process and the Cochrane Central Register of Controlled Trials were searched. Selected publications were reviewed for inclusion by two independent expert reviewers. Discrepancies were reconciled by a third independent reviewer. Data from selected studies were extracted using a predefined grid to ensure uniform and comparable results were captured. RESULTS: A systematic search (cut-off date of 2 May 2016) identified 20 studies (13 observational; seven randomized controlled trials). All studies were of sufficient quality to include in this analysis and comprised 1221 participants, with 5981 bleeds in 746 individuals treated with rFVIIa. Haemostatic overall effectiveness of the individual studies identified ranged from 68% to 100% at ≤12 hours, 86% to 96% at 13-24 hours and 76% to 99% at 24-48 hours with rFVIIa <100 μg/kg, with similar rates reported for the ≥250 μg/kg dose. However, heterogeneity between the studies precluded pooling of results. CONCLUSIONS: Data from the individual studies confirmed that rFVIIa is an effective therapy for the on-demand treatment of bleeds in congenital haemophilia with inhibitors. However, the high levels of heterogeneity between studies precluded pooling of results for a valid, reliable or precise summary measure. There remains a need to implement standardized clinical definitions and measurements for the effectiveness and safety of haemophilia therapies in future clinical trials.
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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 | 0.599 | 0.781 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.019 |
| Bibliometrics | 0.033 | 0.035 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".