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Record W2800803547 · doi:10.5206/uwomj.v86i1.2132

Clinical telemedicine utilization in paediatric nephrology at a tertiary care centre

2017· article· en· W2800803547 on OpenAlexvenueaboutno aff
Fei Shao, Jody Andody, April Reed, Guido Filler

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTelemedicineMedicineSubspecialtyPopulationNephrologyTelehealthFamily medicineEmergency medicineInternal medicineHealth care

Abstract

fetched live from OpenAlex

Purpose: Telemedicine is an emerging tool that offers medical consultations to patients with limited access to subspecialty care. To date, the use of telemedicine in Canadian medical practices has not been studied in depth. This study aims to characterize the three different telemedicine clinic models in use at the paediatric nephrology program at the Children’s Hospital of Western Ontario and the patient population they served.
 Methods: To complete this retrospective study, all paediatric nephrology telemedicine consultations provided by one physician from September 30, 2010 to July 13, 2016 were analyzed, which comprised 264 separate consultations with 87 patients.
 Results: The number of consults increased from 0.25 per month to 8.78 per month between 2010 and 2016, with the most substantial increase seen from 2014 onwards with the establishment of the Windsor block clinics. The patient population had a bimodal age distribution across a geographical distance of 220.3 km to 1371.31 km. Urinary tract infections were the most frequent reason for the consultation.
 Conclusion: Of the three models described in this study, the block clinic model had the most substantial impact on increasing telemedicine services for paediatric nephrology patients.
 
 CORRECTION, September 7, 2017: In the Methods section of this article, the author AR is incorrectly referred to as a nurse practitioner. AR is in fact a registered nurse (RN). We regret that this error was included.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.291
Teacher spread0.264 · 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.

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

Citations2
Published2017
Admission routes2
Has abstractyes

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