Patient data meta‐analysis of Post‐Authorization Safety Surveillance (PASS) studies of haemophilia A patients treated with <scp>rAHF</scp>‐PFM
Bibliographic record
Abstract
UNLABELLED: A Post-Authorization Safety Study (PASS) global program was designed to assess safety and effectiveness of rAHF-PFM (ADVATE) use in haemophilia patients in routine clinical settings. The main aim of this project was to estimate the rate of inhibitors and other adverse events across ADVATE-PASS studies by meta-analysing individual patient data (IPD). Eligible Studies: PASS studies conducted in different countries, between 2003 and 2013, for which IPD were provided. Eligible patients: haemophilia A patients with baseline FVIII:C < 5%, with a known number of prior exposure days (EDs). PRIMARY OUTCOME: de novo inhibitors in severe, previously treated patients (PTPs) with > 150 EDs. SECONDARY OUTCOMES: de novo inhibitors according to prior exposure and disease severity; other adverse events; annualized bleeding rate (ABR). ANALYSIS: random-effects logistic regression. Five of seven registered ADVATE-PASS (Australia, Europe, Japan, Italy and USA) and 1188 patients were included (median follow-up 384 days). Among severe PTPs with > 150 EDs, 1/669 developed de novo inhibitors (1.5 per 1000; 95% confidence interval [CI] 0.2, 10.6 per 1000). Among all patients included in the PASS studies, 21 developed any type of inhibitors (2.0%, 95% CI: 0.8%, 4.7%). Less than 1% of patients presented with other serious adverse events possibly related to ADVATE. The overall median ABR was 3.83 bleeds/year (first, third quartiles: 0.60, 12.90); 1.66 (0, 4.78) in the 557 patients continuously on prophylaxis ≥ twice/week. Meta-analysing PASS data from different countries confirmed the overall favourable safety and effectiveness profile of ADVATE in routine clinical settings.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".