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Record W221729550 · doi:10.2450/2013.0261-12

Impact of changes to policy for Mexican risk travel on Canadian blood donor deferrals.

2013· article· en· W221729550 on OpenAlexaffabout
Sheila F. O’Brien, Samra Uzicanin, Karine Choquet, Qilong Yi, Wenli Fan, Mindy Goldman

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsCanadian Blood Services
Fundersnot available
KeywordsDeferralMalariaGeographyDestinationsPopulationDemographyDonationSocioeconomicsTourismMedicineEnvironmental healthEconomic growthBusinessEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Travel to malaria risk areas such as Mexico is a common source of donor deferral in Canada. On February 21st, 2011 the deferrable regions in Mexico were revised to permit donation if donors travelled to the state of Quintana Roo, Mexico, a popular ocean-side resort area. MATERIALS AND METHODS: Canadian travel data and malaria deferral rates since 2007 were plotted to examine trends. Deferral records in one centre were accessed from January to April, 2011 to tabulate travel destinations of deferred donors immediately before and after the change. RESULTS: Travel to Mexico and the Caribbean accounts for 63% of general population travel, and travel to Mexico has been increasing (P <0.05). Deferral for short-term malaria risk travel has a strong seasonal trend with peaks in the winter and troughs in the summer. Approximately 36,000 fewer donations were lost following the change, a reduction of 37% from the previous year. Deferrals in one centre increased for Caribbean/Central America after the change (P <0.05) consistent with the seasonal trend, but decreased for Mexico (P <0.05). DISCUSSION: Deferrals for malaria risk travel are substantial. Careful revision and refinement of risk areas of travel can significantly reduce the burden of deferral.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.033
GPT teacher head0.253
Teacher spread0.220 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

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