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Variations in pretransfusion practices

2003· article· en· W2422100644 on OpenAlexaff
B.J. Padget, Judith Hannon

Bibliographic record

VenueImmunohematology · 2003
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsCanadian Blood Services
Fundersnot available
KeywordsBlood typingMedicineTest (biology)Medical emergencyTransfusion medicineBest practiceBlood transfusionOperations managementSurgeryEngineeringPolitical science

Abstract

fetched live from OpenAlex

A variety of pretransfusion tests have been developed to improve the safety and effectiveness of transfusion. Recently, a number of traditional tests have been shown to offer limited clinical benefit and have been eliminated in many facilities. A survey of pretransfusion test practices was distributed to 116 hospital transfusion services. Routine test practices and facility size were analyzed. Ninety-one responses were received. Many smaller laboratories include tests such as anti-A,B, an autocontrol, and DAT, and immediate spin and 37 degrees Celsius microscopic readings. Nine percent never perform an Rh control with anti-D typing on patient samples. Various antibody screening and crossmatch methods are utilized. Individual laboratory test practices should be periodically assessed to ensure that they comply with standards, represent the recognized best practice, and are cost-effective. The survey responses indicate that many laboratories perform tests that are not necessary or cost-effective. These facilities should review their processes to determine which tests contribute to transfusion safety. Smaller facilities may be reluctant to change or lack the expertise necessary for this decision making and often continue to perform tests that have been eliminated in larger facilities. Consultation with larger hospital transfusion services may provide guidance for this change.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.021
GPT teacher head0.312
Teacher spread0.291 · 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 designObservational
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

Citations1
Published2003
Admission routes1
Has abstractyes

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