The association between blood donor sex and age and transfusion recipient mortality: an exploratory analysis
Bibliographic record
Abstract
BACKGROUND: There is recent support for long-term adverse effects of donor-recipient sex-mismatched red blood cell (RBC) transfusion, but short-term impact is unknown. A retrospective exploratory analysis was performed using data from a research database. METHODS: Adults admitted to hospitals in one Canadian center who received RBCs (2008-2014 [3 sites]; 2012-2014 [1 site]) were eligible. Patient data were extracted from a research database and donor data from the blood supplier. Cox regression models were used, with control of risk and confounding variables as covariates or using stratification. Exposure was defined by mutually exclusive categories. The outcome was in-hospital mortality. RESULTS: A total of 25,219 adults received 97,886 RBCs. Diagnoses included cardiovascular (28.8%), neoplastic (15.6%), traumatic (15.4%), or gastrointestinal (10.5%); 56.3% of transfused RBCs were male donors, and median donor age was 45 years (interquartile range, 30-54). Female patients exposed to male RBCs experienced a higher risk of in-hospital death (hazard ratio [HR], 1.31; 95% confidence interval [CI], 1.02-1.69; p = 0.038) compared to exclusive female RBC exposure. Exposure to RBCs from donors aged 45 years or younger was associated with a higher in-hospital death (HR, 1.21; 95% CI, 1.02-1.44; p = 0.026) compared to exclusive RBC exposure to donors older than 45 years. Donor-recipient sex-mismatched RBC exposure (vs. exclusively sex-matched) and RBC exposure from donors aged 45 years or younger (vs. exclusively RBCs from donors >45) were associated with increased mortality: sex-mismatched (HR, 1.23; (95% CI, 1.04-1.45; p = 0.017); donors aged 45 years or younger (HR, 1.21; (95% CI, 1.02-1.43; p = 0.031). CONCLUSION: Donor-recipient sex-matched RBC transfusions and transfusions from older donors may benefit patients.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".