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Consenting to blood: what do patients remember?

2005· article· en· W2051998156 on OpenAlexaff
T. K. Chan, Kerena Eckert, P. Venesoen, Ken Leslie, Ian Chin‐Yee

Bibliographic record

VenueTransfusion Medicine · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsCanadian Blood ServicesLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsRecallInformed consentMedicineBlood transfusionFamily medicineMedical emergencyAlternative medicinePsychologySurgery

Abstract

fetched live from OpenAlex

We sought to characterize the consent process for transfusion and determine its impact on patients' knowledge and level of comfort with receiving blood. We identified all adult patients who had received blood transfusion at a tertiary care centre over 3 months. Patients who were discharged each received a survey that assessed their (1) recall of the consent process, (2) recall of information conveyed, (3) assessment of the discussion's understandability and (4) perceived knowledge of as well as comfort level with transfusion as a result of the discussion. Overall, 80% of respondents recalled discussing and signing an informed consent. Information was mostly conveyed by attending physicians (35%) and consent obtained in the patient's hospital room (38%) or the preadmission clinic (19%). Although the majority recalled the consent process, many did not recall the discussion of specific transfusion risks or alternatives to donor blood (88%). Although the majority felt the discussion was at least somewhat understandable (77%), only 35% felt better informed and more comfortable with accepting blood. Despite implementation of written informed consent for transfusion, patients' recollection and understanding of risks and alternatives remain poor. This suggests the need for improving risk communication.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.157
GPT teacher head0.428
Teacher spread0.271 · 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 designNot applicable
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

Citations30
Published2005
Admission routes1
Has abstractyes

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