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Record W2097424453 · doi:10.4103/0973-6247.42696

The approach taken to reducing the risk of transfusion related acute lung injury in Canada

2008· article· en· W2097424453 on OpenAlexaffabout
GH Growe, TR Petraszko, Mark Bigham

Bibliographic record

VenueAsian Journal of Transfusion Science · 2008
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsCanadian Blood Services
Fundersnot available
KeywordsTransfusion-related acute lung injuryMedicineReferralIntensive care medicineIncidence (geometry)Blood transfusionMedical emergencyEmergency medicineFamily medicineLungSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Transfusion related acute lung injury (TRALI) has become a major reported cause of severe transfusion reactions and mortality. Over the past four years significant changes have been taken in Canada in order both to improve the recognition of the risk and to decrease its incidence. An international meeting was held in April of 2004 entitled "Towards an Understanding of TRALI". As a result of the analysis and recommendations from this meeting, the Canadian Blood Services established an ongoing review committee and established a laboratory diagnostic facility to identify at risk donors and recipients. A system has been developed to identify implicated donors and exclude them from the blood donor pool. Other steps have been taken to exclude potentially high risk donors, such as previously pregnant females, from the plasma and platelet donor pool. A considerable amount of education also has been offered to clinical services in the country. This paper summarizes the definitions, categorizations of implicated donors, and the ongoing precautionary activities related to plasma products. Noted within the article are the methods used for locating and selecting data. These were primarily based on the international TRALI conference in 2004, and from ongoing discussions and information provided by the Canadian Blood Services TRALI Review Committee. No ethics referral or approval was requested, and a summary is included in the article.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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

Citations3
Published2008
Admission routes2
Has abstractyes

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