Comparison of deferral rates using a computerized versus written blood donor questionnaire: a randomized, cross-over study [ISRCTN84429599]
Bibliographic record
Abstract
BACKGROUND: Self-administered computer-assisted blood donor screening strategies may elicit more accurate responses and improve the screening process. METHODS: Randomized crossover trial comparing responses to questions on a computerized hand-held tool (HealthQuiz, or HQ), to responses on the standard written instrument (Donor Health Assessment Questionnaire, or DHAQ). Randomly selected donors at 133 blood donation clinics in the area of Hamilton, Canada participated from 1995 to 1996. Donors were randomized to complete either the HQ or the DHAQ first, followed by the other instrument. In addition to responses of 'yes' and 'no' on both questionnaires, the HQ provided a response option of 'not sure'. The primary outcome was the number of additional donors deferred by the HQ. RESULTS: A total of 1239 donors participated. Seventy-one potential donors were deferred as a result of responses to the questionnaires; 56.3% (40/71) were deferred by the DHAQ, and an additional 43.7% (31/71) were deferred due to risks identified by the HQ but not by the DHAQ. Fourteen donors self-deferred; 11 indicated on the HQ that they should not donate blood on that day but did not use the confidential self-exclusion option on the DHAQ, and three used the self-exclusion option on the DHAQ but did not indicate that they should not donate blood on the HQ. The HQ identified a blood contact or risk factor for HIV/AIDS or sexually transmitted infection that was not identified by the DHAQ in 0.1% to 2.7% of donors. CONCLUSION: A self-administered computerized questionnaire may increase risk reporting by blood donors.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".