Outcomes of long‐term von Willebrand factor prophylaxis use in von Willebrand disease: A systematic literature review
Bibliographic record
Abstract
BACKGROUND: Von Willebrand Disease (VWD) is a common inherited bleeding disorder. Patients with VWD suffering from severe bleeding may benefit from the use of secondary long-term prophylaxis. AIM: Systematically summarize the evidence on the clinical outcomes of secondary long-term prophylaxis in patients with VWD and severe recurrent bleedings. METHODS: We searched Medline and EMBASE through October 2019 for relevant randomized clinical trials (RCTs) and comparative observational studies (OS) assessing the effects of secondary long-term prophylaxis in patients with VWD. We used Cochrane Risk of Bias (RoB) tool and the RoB for Non-Randomized Studies of interventions (ROBINS-I) tool to assess the quality of the included studies. We conducted random-effects meta-analyses and assessed the certainty of the evidence using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. RESULTS: We included 12 studies. Evidence from one placebo controlled RCT suggested that VWD prophylaxis as compared to no prophylaxis reduced the rate of bleeding episodes (Rate ratio [RR], .24; 95% confidence interval [CI], .17-.35; low certainty evidence), and of epistaxis (RR, .38; 95%CI, .21-.67; moderate certainty evidence), and may increase serious adverse events RR 2.73 (95%CI .12-59.57; low certainty). Evidence from four before-and-after studies in which researchers reported comparative data suggested that VWD prophylaxis reduced the rate of bleeding (RR .34; 95%CI, .25-.46; very low certainty evidence). CONCLUSION: VWD prophylaxis treatment seems to reduce the risk of spontaneous bleeding, epistaxis, and hospitalizations. More RCTs should be conducted to increase the certainty in these benefits.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".