Risk factors for red blood cell alloimmunization in the Recipient Epidemiology and Donor Evaluation Study (<scp>REDS</scp>‐<scp>III</scp>) database
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the significance of red blood cell (RBC) alloimmunization, the lack of standardized registries in the US has prevented the completion of large studies. Data from 3·5 years of the Recipient Epidemiology and Donor Evaluation Study-III (REDS-III) recipient database, containing information from 12 hospitals, were studied. A RBC alloantibody responder had an antibody identified at any point during the study, and a non-responder had a negative antibody screen at least 15 days post-RBC transfusion. Demographics, blood type, ICD9/10 codes, and other potential correlates were evaluated. Of 319 177 (2·07%) screened patients, 6597 had a total of 8892 clinically significant RBC alloantibodies identified, with 75% being in the Rh or Kell families. Alloimmunization was more common in females (2·38%) than males (1·68%), and in RhD negative (2·82%) than RhD positive (1·94%) patients. Age, sex, RhD status and race were associated with being a responder, and certain diagnoses (including sickle cell disease or trait, systemic lupus erythematosus, rheumatoid arthritis and myelodysplastic syndrome) were more common among responders than non-responders. Data collected in this multi-centre recipient database provide the largest RBC alloimmunized patient cohort studied in the US, with previously known demographic and disease associations of responder status confirmed, and new associations identified.
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.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it