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Record W2072375059 · doi:10.1258/135763306776738611

Telestroke: a multi-site, emergency-based telemedicine service in Ontario

2006· article· en· W2072375059 on OpenAlexaffabout
K. Waite, Frank L. Silver, Cheryl Jaigobin, Sandra E. Black, Liesly Lee, Brian J. Murray, Peter Danyliuk, Edward M. Brown

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2006
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity Health NetworkHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsTelemedicineMedical emergencyMedicineVideoconferencingAcute strokeService (business)ThrombolysisTelehealthEmergency medicineEmergency medical servicesStroke (engine)Emergency departmentHealth careNursingTelecommunicationsBusinessMyocardial infarctionInternal medicine

Abstract

fetched live from OpenAlex

A telestroke service was established in Ontario in 2002. Six neurologists on four campuses of two academic health centres of the University of Toronto participated in the call roster to support emergency physicians in two northern cities, North Bay and Sudbury. Videoconferencing units were provided in the hospitals and in the homes of the neurologists. PC workstations were used to access computed tomography (CT) images. In the first 34 months' operation, a total of 88 patient consultations were conducted. Twenty-six patients received tissue plasminogen activator (t-PA). Although the number of consultations was relatively low, the feasibility of telemedicine for acute stroke care was demonstrated. The economics remain to be explored. The telestroke model is a viable alternative to the provision of acute stroke care for communities that have CT scanners, but no access to a resident neurologist.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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

Citations59
Published2006
Admission routes2
Has abstractyes

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Same venueJournal of Telemedicine and TelecareSame topicAcute Ischemic Stroke ManagementFrench-language works237,207