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Record W1982231401 · doi:10.1258/135763304773391503

A cost-effectiveness analysis of interactive paediatric telecardiology

2004· article· en· W1982231401 on OpenAlexaff
Claude Sicotte, Pascale Lehoux, Nicolaas van Doesburg, Godefroy Cardinal, Yves Leblanc

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2004
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de Montréal
Fundersnot available
KeywordsMedicineComputer scienceMedical emergency

Abstract

fetched live from OpenAlex

We analysed the cost-effectiveness of a teleconsultation service after five years of operation. The service provides diagnostic consultation at a distance for children suffering from cardiac pathologies. A retrospective study was performed with all 78 infants who had received a paediatric cardiology teleconsultation over a four-year period from January 1998. The cost-effectiveness of telecardiology was compared with that of the conventional means of providing services. Teleconsultation proved to be an effective and reliable method of enhancing access to tertiary care. The number of patient journeys (both emergency transfers and semi-urgent or elective visits to the tertiary care centre) was reduced by 42%. However, the cost analysis demonstrated that teleconsultation did not result in overall cost savings: the total cost of telecardiology was C dollars 272,327 and the total cost of conventional care would have been C dollars 157,212. There were direct savings for patients but not for the health-care system, because of the high cost of the equipment and telecommunication fees. Telemedicine therefore represented a supplementary cost of C dollars 1500 per patient. In summary, telemedicine added to cost but increased effectiveness. The incremental cost-effectiveness ratio of teleconsultation was estimated to C dollars 3488 per patient journey avoided.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.172
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.380
Teacher spread0.346 · 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

Citations32
Published2004
Admission routes1
Has abstractyes

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