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Patients’ Knowledge Of Anticoagulation and Its Association With Clinical Characteristics, INR Control and Warfarin-Related Adverse Events

2013· article· en· W116679998 on OpenAlexaff
Poupak Rahmani, Charlotte Guzman, Mark Blostein, Ashley Tabah, Alla Muladzanov, Susan R. Kahn

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcGill University Health CentreJewish General HospitalMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineWarfarinAdverse effectThrombosisTest (biology)Internal medicinePediatricsEmergency medicineAtrial fibrillation

Abstract

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Abstract Background Whether level of knowledge of anticoagulation (AC) among patients on warfarin plays a role in maintenance of therapeutic INR or in warfarin-related adverse events is controversial. Most studies conducted on this subject had small patient sample sizes and did not use validated questionnaires to assess patients’ knowledge of AC. Objectives To use the validated Oral Anticoagulation Knowledge (OAK) test (Zeolla MM, 2006) to assess knowledge of AC among patients attending a busy AC clinic, and to examine associations between level of knowledge, INR control and adverse events. We hypothesized that patients with higher OAK test scores (i.e. greater knowledge) would have better INR control (primary outcome) and fewer bleeding and thrombosis events (secondary outcomes). Methods Consecutive patients who had been followed in our AC clinic (tertiary care, university-affiliated hospital, 20,000 patient-visits per year) for at least one year and consented to participate were asked to complete the OAK test. The OAK test is a 20-question multiple-choice questionnaire that assesses patients’ knowledge of warfarin AC. A passing score is ≥15 correct responses. Patient charts were reviewed to obtain data on clinical and demographic characteristics, and information on INR values and any thrombosis or bleeding events during the preceding 1 year period. Associations between OAK scores and patient characteristics, INR control and bleeding/thrombosis events were assessed by chi-square and t-tests, as appropriate. Results Among 252 patients screened for participation, 225 met the inclusion criteria and completed the OAK test. Mean (SD) age was 70 (13.4) years, 53% were male and 75% were on warfarin for >3 years. Indications for AC were atrial fibrillation in 65%, VTE in 8%, mechanical heart valve in 10%, and other in 19%. The mean OAK score was 12/20, and 64% failed the OAK test. Predictors of a pass score on the OAK test were younger age (p= 0.01) and higher level of education (p=0.03). Over the preceding year, 57.3% of INRs were therapeutic, 25.1% subtherapeutic and 17.4% supratherapeutic, and there were 22 bleeding events and 5 thrombosis events. There was no association between OAK score and INR control, or OAK score and bleeding or thrombosis events. Conclusion To our knowledge, this is the first study to use the validated OAK test to assess patients’ AC knowledge. We found that younger and more educated patients were more likely to pass the OAK test; however, OAK test result did not predict INR control or occurrence of bleeding or thrombotic events. The OAK test may not be sensitive enough to capture the standard of care practiced in different anticoagulation clinics (e.g. differences in teaching material, frequency of INR checks in stable patients). Also, for some patients, AC knowledge among their caretakers may be more important than self-knowledge. Further research is needed to assess the relationship between AC knowledge, INR control and adverse clinical outcomes. Disclosures: No relevant conflicts of interest to declare.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0040.000

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 head0.026
GPT teacher head0.297
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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".

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Citations11
Published2013
Admission routes1
Has abstractyes

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