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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".