Factors associated with a second deferral among donors eligible for re‐entry after a false‐positive screening test for syphilis, <scp>HCV</scp>,<scp> HBV</scp> and <scp>HIV</scp>
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Since 25 May 2010, all donors at our blood centre who tested false-positive for HIV, HBV, HCV or syphilis are eligible for re-entry after further testing. Donors who have a second false-positive screening test, either during qualification for or after re-entry, are deferred for life. This study reports on factors associated with the occurrence of such deferrals. MATERIALS AND METHODS: Rates of second false-positive results were compared by year of deferral, transmissible disease marker, gender, age, donor status (new or repeat) and testing platform (same or different) both at qualification for re-entry and afterwards. Chi-square tests were used to compare proportions. Cox regression was used for multivariate analyses. RESULTS: Participation rates in the re-entry programme were 42·1%: 25·6% failed to qualify for re-entry [different platform: 2·7%; same platform: 42·9% (P < 0·0001)]. After re-entry, rates of deferral for second false-positive results were 8·4% after 3 years [different platform: 1·8%; same platform: 21·4% (P < 0·0001)]. Deferral rates were higher for HIV and HCV than for HBV at qualification when tested on the same platform. The risk, when analysed by multivariate analyses, of a second deferral for a false-positive result, both at qualification and 3 years after re-entry, was lower for donors deferred on a different platform; this risk was higher for HIV, HCV and syphilis than for HBV and for new donors if tested on the same platform. CONCLUSION: Re-entry is more often successful when donors are tested on a testing platform different from the one on which they obtained their first false-positive result.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".