Impact of Fidanacogene Elaparvovec Gene Therapy on Joint Health in Adults With Haemophilia B: Results From a Phase 3 Study
Bibliographic record
Abstract
Davide Matino reports research grants paid directly to the Institution (McMaster University) from Bayer, Pfizer, Novo Nordisk, Octapharma, Roche, Sanofi and Spark; personal fees/honoraria from Sanofi, Sobi, Novo Nordisk, Bayer, Pfizer, Octapharma and Roche for participation in advisory boards, lectures and preparation of educational material. Laurent Frenzel has received grants from CSL Behring and Pfizer; and consulting fees BioMarin, CSL Behring and Pfizer. Ali Bulent Antmen has participated in scientific advisory boards and speaker's bureaus for BioMarin, CSL Behring, Novo Nordisk, Pfizer, Roche, Sobi and Takeda. Jan Astermark has received consulting fees and honoraria from Bayer, BioMarin, CSL Behring, Novo Nordisk, Octapharma, Pfizer, Roche, Sanofi, Shire/Takeda, Sobi and Uniqure. Monica H. Cerqueira has participated in advisory Boards for Bayer, BioMarin, Novo Nordisk, Roche and Takeda; participated in research sponsored by Bayer, BioMarin and Pfizer; and served as a speaker sponsored by Novo Nordisk. Adam Cuker has received consulting fees from MingSight, Pfizer, Sanofi and Synergy; and received authorship royalties from UpToDate. Olga Katsarou-Fasouli has received honoraria from Bayer and Sobi and participated on Data Safety Monitoring Boards/Advisory Boards for Novo Nordisk and Sobi. Kaan Kavakli has participated in Scientific Advisory Board meetings for BioMarin, CSL Behring, Novo Nordisk, Pfizer, Roche and Takeda. Margareth C. Ozelo has received research grants from Pfizer, Roche and Takeda; participated as a clinical trial investigator for BioMarin, Novo Nordisk, Pfizer, Roche, Sanofi and Takeda; received speaker honoraria from Bayer, Novo Nordisk, Roche and Takeda; received consulting fees from Bayer, BioMarin, Novo Nordisk, Pfizer, Roche and Takeda; and participated in grant reviewing for Grifols and Pfizer. Stephanie P'ng has received honoraria from and conducted clinical trials on behalf of CSL Behring, Novo Nordisk, Pfizer, Roche, Sanofi and Takeda. Jiaan-Der Wang has received honoraria from and conducted clinical trials on behalf of Bayer, Chugai, CSL Behring, Pfizer, Novo Nordisk and Sanofi. Delphine Agathon, Francesca Biondo, Pascal Klaus, John McKay, Jeremy Rupon, Pengling Sun, Lisa J. Wilcox and Frank Plonski are employees and shareholders of Pfizer. Upon request, and subject to review, Pfizer will provide the data that support the findings of this study. Subject to certain criteria, conditions, and exceptions, Pfizer may also provide access to the related individual de-identified participant data. See https://www.pfizer.com/science/clinical-trials/trial-data-and-results for more information. Supporting Figure S1: Change from baseline in HJHS total score over time Supporting Figure S2: Correlation between FIX level (one-stage assay, SynthASil reagent) and HJHS total score change from baseline to Year 1 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".