P‐BB‐51 | Increasing Blood Diversity: How Blood Center Websites Tackle Critical Blood Shortages for Sickle Cell Patients Through Recruitment of African‐American Donors
Bibliographic record
Abstract
Background/Case Studies: An anti-treponemal-specific IgM/IgG test is used to screen blood donations for syphilis.Previously, we identified a temporal association between unconfirmed syphilis repeat-reactive (RR) results and other potential confounders: vaccination campaigns for influenza/COVID-19, community-circulating influenza virus (I), COVID-19, and other respiratory viruses (ORV) typically overlapping with influenza (IORV).In this study we assessed the unconfirmed peak syphilis RR rates in the COVID-19 pandemic period compared to the pre-pandemic period.Study Design/Methods: Influenza and COVID-19 vaccination histories in the preceding 3 months were extracted.Aggregated syphilis RR results that did not confirm were collated (September 2017 to December 2022; PK 7300 instrument [Beckman Coulter; Brea, CA, USA]).Peak syphilis RR results were compared against; influenza/COVID-19 vaccination data.IORV and Respiratory Syncytial Virus (RSV) data were acquired from the Public Health Agency of Canada Respiratory Virus Detection Surveillance System.Data analysis used GraphPad Prism 9.5.0 (GraphPad Software, Boston, MA, USA).Results/Findings: Results and findings are listed in the Table.Conclusions: Multiple immune pressures in the postpandemic period are associated with a noticeable increase in syphilis RR rates in the pandemic period compared to the pre-pandemic period.We speculate that re-emerging immune pressures from vaccination and/or community-acquired COVID-19 or IORV (including RSV) are generating non-specific immune responses identified by the IgM component of the syphilis test.
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 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.006 |
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".