Assessment of risk of variant creutzfeldt‐Jakob disease (vCJD) from use of bovine heparin
Bibliographic record
Abstract
Abstract Purpose In the late1990s, reacting to the outbreak of bovine spongiform encephalopathy (BSE) in the United Kingdom that caused a new variant of Creutzfeldt‐Jakob disease (vCJD) in humans, manufacturers withdrew bovine heparin from the market in the United States. There have been growing concerns about the adequate supply and safety of porcine heparin. Since the BSE epidemic has been declining markedly, the US Food and Drug Administration reevaluates the vCJD risk via use of bovine heparin. Methods We developed a computational model to estimate the vCJD risk to patients receiving bovine heparin injections. The model incorporated information including BSE prevalence, infectivity levels in the intestines, manufacturing batch size, yield of heparin, reduction in infectivity by manufacturing process, and the dose‐response relationship. Results The model estimates a median risk of vCJD infection from a single intravenous dose (10 000 USP units) of heparin made from US‐sourced bovine intestines to be 6.9 × 10−9 (2.5‐97.fifth percentile: 1.5 × 10−9‐4.3 × 10−8), a risk of 1 in 145 million, and 4.6 × 10−8 (2.5‐97.fifth percentile: 1.1 × 10−8‐2.6 × 10−7), a risk of 1 in 22 million for Canada‐sourced products. The model estimates a median risk of 1.4 × 10−7 (2.5‐97.fifth percentile: 2.9 × 10−8‐9.3 × 10−7) and 9.6 × 10−7 (2.5‐97.fifth percentile: 2.1 × 10−7‐5.6 × 10−6) for a typical treatment for venous thromboembolism (infusion of 2‐4 doses daily per week) using US‐sourced and Canada‐sourced bovine heparin, respectively. Conclusions The model estimates the vCJD risk from use of heparin when appropriately manufactured from US or Canadian cattle is likely small. The model and conclusions should not be applied to other medicinal products manufactured using bovine‐derived materials.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".