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Record W174537503

Patient self-management of oral anticoagulation: a review.

2003· article· en· W174537503 on OpenAlexaff
Rubina Sunderji, Anthony Fung, Kenneth Gin, Karen Shalansky, Cedric Carter

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineWarfarinNomogramIntensive care medicinePoint of careSelf-managementPatient satisfactionClinical trialMedical emergencySurgeryNursingInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Self-management of warfarin is an evolving strategy that involves self-testing of the international normalized ratio using a point-of-care device and adjustment of warfarin dosage by the patient using a dosage-adjustment nomogram. There is mounting evidence from clinical trials that self-management of warfarin is feasible and is potentially superior to conventional management by physicians in maintaining anticoagulation control. Some advantages of this strategy are convenience, rapid availability of results with timely adjustment of warfarin dosages, increased patient responsibility for their own therapy and enhanced patient satisfaction. Access to point-of-care instruments may prove particularly valuable for patients without ready access to laboratories, frequent travellers who are often away from their home laboratory for extended periods of time and those who experience difficulties with venous blood collection. Self-management may be considered for carefully selected and properly trained individuals. Information from several ongoing clinical trials will aid in determining the value of anticoagulation self-management with respect to complication rates and economic outcomes.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.058
GPT teacher head0.304
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2003
Admission routes1
Has abstractyes

Explore more

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