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Record W1908166573 · doi:10.1111/vox.12002

Donation by donors with an atypical pulse rate does not increase the risk of cardiac ischaemic events

2012· article· en· W1908166573 on OpenAlexaff
Marc Germain, Gilles Delage, Yves Grégoire, Pierre Robillard

Bibliographic record

VenueVox Sanguinis · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsHéma-Québec
Fundersnot available
KeywordsMedicineCardiologyIncidence (geometry)Internal medicineOdds ratioPulse (music)Pulse rateDonationBlood pressure

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: In many jurisdictions, blood donors who have an atypical pulse rate are temporarily deferred. This practice is not supported by evidence. We evaluated whether accepting donors with an atypical pulse rate increases their risk of cardiac ischaemic events. METHODS: We measured the cumulative incidence of hospitalizations and deaths for coronary heart disease within 1 year of follow-up among donors who, between 2002 and 2006, were temporarily deferred because of an atypical pulse (<50 beats/min, >100 beats/min, or irregular). We compared this incidence to that observed among donors who also had an atypical pulse but who were allowed to donate, following a change in our deferral policy in 2007. The occurrence of cardiac events was determined through hospital discharge and death registries. RESULTS: Among 6076 donors who were temporarily deferred for an atypical pulse, the 1-year rate of hospitalization or death for cardiac ischaemic events was 3.5/1000, compared to 2.4 in donors who had an atypical pulse but who were allowed to donate (n =10,671), for an adjusted odds ratio of 1.7 (95% CI, 0.9-3.0, P=0.08). CONCLUSION: Regardless of the clinical significance of an atypical pulse rate, our data show that accepting donors with this condition does not increase the occurrence of serious cardiac ischaemic events. We conclude that pulse rate measurement in prospective donors is not warranted.

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 score0.288
Threshold uncertainty score0.521

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 head0.011
GPT teacher head0.232
Teacher spread0.222 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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