Bibliographic record
Abstract
Devices for extracting blood from donors require rigorous evaluation for the safety of donors. Complications arise from the fact that outcomes on successive donations from the same donor are not independent, adverse event rates are extremely rare, and there is substantial heterogeneity in the propensity for donors to donate over time. We develop a statistical framework for the design of a superiority trial and a non-inferiority trial, aiming to demonstrate the safety of a new donation device compared to the standard one. Historical data on the intensity and heterogeneity of donation across donors, the adverse event rate, and the serial dependence in adverse events provide information on how to plan accrual and follow-up periods to give the expected number of donors and donations. The analysis is based on a binary donation-specific outcome modeled with a Poisson approximation (i.e., log link and identity variance function) using generalized estimating equations with a working independence assumption. The historical data enables calculation of the asymptotic robust variance estimate, which is used for planning. The formulae derived are found to provide good control of the type I error rate and statistical power. We illustrate the derivations with application to a plasma donation trial aiming to investigate the safety of a new device, with the outcome being serious hypotensive adverse events.
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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 | 0.210 | 0.275 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".