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Record W2186559653

Iron status in Canadian Blood Services donors

2013· article· en· W2186559653 on OpenAlexaboutno aff
Mindy Goldman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsnot available
Fundersnot available
KeywordsBlood donorBlood donationsMedicineDonationFingerstickIron statusPopulationBlood transfusionHemoglobinDemographyIron deficiencySurgeryEnvironmental healthImmunologyAnemiaInternal medicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Iron is an essential nutrient, yet iron balance is precarious for much of the world’s population. In North America, iron deficiency is common in distinct population groups, such as women of child-bearing age. 1 Transfusion therapy is a cornerstone of modern medical care. Canada’s need for fresh blood components, including red blood cells, is met by approximately 600,000 volunteer donors. Canadian Blood Services (CBS) collects, processes, tests, and distributes blood components to all provinces and territories, except Quebec. Each year, CBS collects approximately 950,000 units of whole blood of 500 mL each. A dedicated group of repeat donors provide close to 90% of donations, while 10% of donations come from first time donors. The average donation frequency in 2012 was slightly over 2 donations per donor yearly. Donor hemoglobin (Hb) screening is performed on a fingerstick capillary sample prior to each donation, with a minimum qualifying level of 125 g/L required for both male and female donors, and 8% of female donors and 0.5% of male donors are temporarily deferred for inadequate Hb level. Each whole blood donation results in the loss of 225 to 250 mg of iron. However, no routine Key points

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.998

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 communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.003

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 head0.007
GPT teacher head0.202
Teacher spread0.194 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2013
Admission routes1
Has abstractyes

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