MétaCan
Menu
Back to cohort
Record W2076666114 · doi:10.4018/jhisi.2013010103

Telehealth Interventions for Management of Chronic Obstructive Lung Disease (COPD) and Asthma

2013· article· en· W2076666114 on OpenAlexaff
Laura Nimmon, Iraj Poureslami, Mark Fitzgerald

Bibliographic record

VenueInternational Journal of Healthcare Information Systems and Informatics · 2013
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of British Columbia HospitalCentre for Advancing Health OutcomesUniversity of British Columbia
Fundersnot available
KeywordsTelehealthPsychological interventionAsthmaCOPDAttendanceMedicineIntervention (counseling)PopulationTelemedicineHealth carePhysical therapyFamily medicineIntensive care medicineMedical emergencyNursingEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

The authors systematically collated, classified, and evaluated the evidence of intervention studies from recent systematic reviews about the effects of telehealth interventions on COPD and asthma care. Eight electronic databases were searched. Eligible articles were those published between 2001 and 2011 in English. Eleven review articles are included. Asthma and COPD are better controlled when patients use interactive technological tools to monitor their chronic disease. The effects of telehealth interventions on emergency department attendance, specific quality of life, and mortality remained less certain. Only some reviews mentioned if the cost-effectiveness was systematically analyzed. Telehealth promises to be a highly effective intervention in managing chronic lung diseases while also potentially reducing some of the economic burdens of asthma and COPD. New directions in telehealth developments, implementations, and evaluations should be made, in which the exchange of health information should not be over simplified, but rather reflect the different socio-cultural practices of population groups and individuals.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.716
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.343
Teacher spread0.321 · 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 teacher head, 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

Citations10
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Healthcare Information Systems and InformaticsSame topicChronic Obstructive Pulmonary Disease (COPD) ResearchFrench-language works237,207