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Record W1616424917

A mobile phone based remote patient monitoring system for chronic disease management.

2007· article· en· W1616424917 on OpenAlexaff
Mathieu Trudel, Joseph A Cafazzo, Melinda Hamill, Walter Igharas, Kevin Tallevi, Peter Picton, Jack Lam, Peter G. Rossos, Anthony Easty, Alexander G. Logan

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsWorkflowMobile phoneFocus groupTelemedicineMedicineDisease managementChronic diseaseMedical emergencyRemote patient monitoringHealth carePhoneComputer scienceIntensive care medicineNursingHealth management systemAlternative medicineBusinessTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Rising concern over the poor state of chronic disease management led to the user-informed design and development of a home tele-monitoring system. Focus groups with patients and primary care providers guided the research team towards a design that would accommodate the workflow and concerns of the healthcare providers and the low use and comfort with technology found among the patient population. The system was trialed in a before-and-after pilot study of 34 patients with diabetes and hypertension. Findings demonstrate a significant improvement in systolic and diastolic blood pressure. An RCT beginning in 2007 is being conducted to confirm these findings. It is hypothesized that this user-centred approach, utilizing focus groups, iterative design and human factors methods of evaluation, will lead to the next-generation of home tele-monitoring applications that are more intuitive, less cumbersome, and ultimately bring about greater patient compliance and better physician management.

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)0.002
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 score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0140.002

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 head0.043
GPT teacher head0.370
Teacher spread0.327 · 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

Citations19
Published2007
Admission routes1
Has abstractyes

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