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Current perceptions of Canadian autologous blood donors

2006· article· en· W2151309939 on OpenAlexaffabout
M Banning, Janis Bormanis, Nedra R. Lander, Doris Neurath, G. Rock

Bibliographic record

VenueVox Sanguinis · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineAutologous bloodDonationBlood transfusionBlood donorBlood supplySurgeryImmunology

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: We determined the perceptions and motivations of autologous donors to establish their regard for this process and the new blood system established in 1999 in Canada. MATERIALS AND METHODS: Patients were surveyed before and after orthopaedic, cardiac, urological or gynecological procedures. RESULTS: Patients (n = 100; 57 men and 43 women) ranged in age from 19 to 83 years. Most had finished high school, and 21 had a university degree. Eighty-four had undergone previous surgery and 27 a previous transfusion. Fifty-one had been blood donors and 17 had been autologous donors. Specialists suggested donation to 78 of the patients. Seventy-two participated because they knew that their blood would be there. Three had a mistrust of the blood provider. Sixty-six believed that there is still a risk of receiving contaminated blood. Sixty-nine thought any risk was > or = 1 in 100,000; however, 16 thought the risk was < 1 in 1000. Postsurgery, 83 were happy to have donated their blood and 77 would do it again. Many felt that it improved their outcome. Most felt an increased sense of safety. Eighty-three patients were confident that the Canadian blood system had improved, but 17 were unsure. CONCLUSIONS: Concern about the safety of the allogeneic blood supply still drives the wish to autodonate. The process gives patients a sense of control and security. A large proportion of people felt that the Canadian blood system had improved, as determined by this 2003 study.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.234
Teacher spread0.219 · 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.

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

Citations4
Published2006
Admission routes2
Has abstractyes

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