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Record W2798647019 · doi:10.3233/978-1-61499-852-5-466

User Evaluation of a Smartphone Application for Anticoagulation Therapy

2018· article· en· W2798647019 on OpenAlexaff
Berglind Smaradottir, Santiago Martínez, Elizabeth M. Borycki, Gareth Loudon, André Kushniruk, Jarle Jortveit, Rune Fensli

Bibliographic record

VenueStudies in health technology and informatics · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceSmartphone applicationHuman–computer interactionMultimedia

Abstract

fetched live from OpenAlex

Anticoagulation therapy with Warfarin is used for specific cardiovascular diseases to control the ability of blood clotting. Traditional ways of self-management therapy are based on paper forms and procedures. This paper presents an evaluation of the smartphone application Warfarin Guide, a computer-assisted decision-support system used to help patients in their management of anticoagulation therapy related to International Normalized Ratio (INR) values. The evaluation consisted of a usability test with 4 participants and a field test with 14 participants who used the application at home during four months. A mixed methods research approach included quantitative and qualitative analysis of the test results. The results showed that participants evaluated the Warfarin Guide as 'useful' for self-management of anticoagulation therapy, reporting key issues for further improvement.

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)0.004
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.166
GPT teacher head0.534
Teacher spread0.369 · 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".

Quick stats

Citations12
Published2018
Admission routes1
Has abstractyes

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