Blood utilization at a national referral hospital in sub‐Saharan Africa
Bibliographic record
Abstract
BACKGROUND: A safe and adequate supply of blood is critical to improving health care systems in sub-Saharan Africa, where little is known about the current use of blood. The aim of this study was to comprehensively describe the use of blood at a tertiary care hospital to inform future efforts to strengthen blood programs in resource-limited settings. STUDY DESIGN AND METHODS: Data were collected from blood bank documentation for all units issued at Mulago Hospital Complex in Kampala, Uganda, from mid-January to mid-April 2014. RESULTS: A total of 6330 units (69% whole blood, 32% red blood cells, 6% platelets, 2% plasma) were issued over the 3-month study period to 3662 unique patients. Transfusion recipients were 58% female and median age was 27 years (interquartile range [IQR], 14-41). Median pretransfusion hemoglobin was 5.6 g/dL (IQR, 4.0-7.2 g/dL, n = 1090). Strikingly, cancer was the top indication for transfusion (33.5%), followed by pregnancy-related complications (12.4%) and sickle cell disease (6.9%). CONCLUSION: This study provides a comprehensive picture of blood use at a national referral hospital in sub-Saharan Africa. Noncommunicable diseases, particularly oncologic conditions, represent a large proportion of demand for transfusion services.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".