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Record W1531070701 · doi:10.1111/trf.13010

Blood utilization at a national referral hospital in sub‐Saharan Africa

2015· article· en· W1531070701 on OpenAlexaff
Elissa K. Butler, Heather Hume, Isaac Birungi, Brenda Ainomugisha, Ruth Namazzi, Henry Ddungu, Isaac Kajja, Susan Nabadda, Jeffrey McCullough

Bibliographic record

VenueTransfusion · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineReferralInterquartile rangeBlood transfusionEmergency medicineMalariaBlood bankHealth careIntensive care medicineFamily medicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.070
GPT teacher head0.268
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2015
Admission routes1
Has abstractyes

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