Predictive ability of sequential surveys in determining donor loss from increasingly stringent variant Creutzfeldt‐Jakob disease deferral policies
Bibliographic record
Abstract
BACKGROUND: Predonation screening questions about travel increase the safety of the blood supply from diseases such as variant Creutzfeldt-Jakob disease (vCJD) and malaria. This study examines the ability of sequential surveys to predict actual travel deferrals and the operational validity of travel questions. STUDY DESIGN AND METHODS: To assess donor travel histories before implementing key deferral policies, two donor surveys were carried out at Canadian Blood Services collection sites in February 1999 (8026 donors) and March 2001 (13,623 donors). In-person interviews were carried out with 1530 donors to assess the operational validity of the short travel question. Time-series analysis was used to determine whether there was a change in deferrals when deferral policies were implemented. Predicted donor loss estimates based on survey results were compared with actual deferrals. RESULTS: Deferrals increased significantly (p < 0.05) when vCJD deferral policies were implemented in October 1999 and September 2001, but not in October 2000. Survey data accurately predicted deferrals 6 months after implementation from the initial policy (2.51% predicted vs. 2.51% actual), but there were fewer deferrals than predicted for the second (2.89% predicted vs. 2.26% actual, p < 0.01) and third deferral policies (3.10% predicted vs. 1.89% actual, p < 0.01). There was 96 percent agreement between donor responses to a short screening question and a detailed travel history. CONCLUSION: The initial survey accurately predicted the actual donor deferral rate, but the deferral rate was less than predicted for subsequent, more stringent donor deferral policies. Donors answered a short travel question suitable for donor screening similarly to a very detailed travel history.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".