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Record W2294416822 · doi:10.1111/trf.13541

The risk of allowing blood donation from men having sex with men after a temporary deferral: predictions versus reality

2016· article· en· W2294416822 on OpenAlexaffabout
Marc Germain

Bibliographic record

VenueTransfusion · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsHéma-Québec
Fundersnot available
KeywordsDeferralDonationMen who have sex with menDemographyHuman immunodeficiency virus (HIV)MedicineEpidemiologyBlood donorDeveloped countryBlood donationsVirologyEnvironmental healthImmunologyEconomicsInternal medicinePopulationEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: In most industrialized countries, men who had sex with men (MSM) are permanently deferred from blood donation. Some countries have adopted a temporary deferral after the MSM behavior, thus avoiding donations made during the window period of human immunodeficiency virus (HIV) infection. However, one concern with this approach is the possible increase in the number of HIV-positive donations obtained from unknowingly infected, abstinent MSM; such donations might inadvertently be made available for transfusion, a risk that was previously estimated through mathematical modeling. STUDY DESIGN AND METHODS: Model predictions were compared to the actual donor rate of HIV in three countries that went from a permanent to a temporary deferral: Australia, the United Kingdom, and Canada. RESULTS: Depending on the model, a temporary deferral should have increased the rate of HIV in the male donor pool by 73% to more than 3400%. In reality, the very low baseline rate of HIV before the change in these three countries (22 cases/year) remained unchanged 2 years after the revised policy (16 cases/year). CONCLUSION: These observations strongly suggest that a temporary deferral for MSM incurs zero risk to recipients, at least in jurisdictions where HIV epidemiology is comparable to that of countries where the change happened.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.235
Teacher spread0.215 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

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