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Record W2069207204 · doi:10.1258/jtt.2010.100614

Use of telemedicine for haemodialysis in very remote areas: the Canadian First Nations

2011· article· en· W2069207204 on OpenAlexaffabout
Claude Sicotte, Khalil Moqadem, Murray Vasilevsky, Johanne Desrochers, Madeleine St-Gelais

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsSante MontrealMcGill University Health CentreUniversité de Montréal
Fundersnot available
KeywordsMultidisciplinary approachBayTelemedicineMedicineHealth careRepeated measures designFamily medicineGeographyStatisticsPolitical science

Abstract

fetched live from OpenAlex

We used a pre-post design to compare the health and care utilization of patients receiving telehaemodialysis services in two James Bay Cree communities. The Cree are an Amerindian First Nation living in the remote James Bay region. The same group of dialysed patients (n = 19) was followed longitudinally over a two-year period: 12 months pre and 12 months post. Analysis of variables measuring the patients' health conditions showed that the quality of care provided was well within recognized good practice guidelines. Repeated measures ANOVA on the variables measuring care utilization showed a significant decrease in the monthly number of medication changes over time (P < 0.01). Different telehaemodialysis models were used in the two communities (virtual patient rounds and telecase reviews with multidisciplinary teams), but they did not lead to differences in health condition or care utilization. This suggests that there is no single prescriptive model for the delivery of tele-expertise.

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)0.004
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 score0.625
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.156
GPT teacher head0.390
Teacher spread0.234 · 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

Citations40
Published2011
Admission routes2
Has abstractyes

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