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Record W3015624539

Internet of Things in Telemedicine: a Discussion Regarding to Several Implementation

2018· article· en· W3015624539 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Information Communication Technologies and Robotic Applications · 2018
Typearticle
Languageen
FieldComputer Science
TopicInternet of Things and AI
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTelemedicineThe InternetProcess (computing)Computer scienceHealth careMultimediaInternet privacyWorld Wide WebPolitical science
DOInot available

Abstract

fetched live from OpenAlex

With the increase of health requirement, the idea of telemedicine turns in to reality. By the help of effective audiovisual and data communication, the process of practice of medical care that includes the delivery of medical care, consultation and treatment, diagnosis, transferring of medical data as well as health education is termed as Telemedicine [1]. Most of the actual implementation of telemedicine system is done by traditional video conference tool, which is somehow becomes not very supportive as far as the complex medical activities are concerned. So this paper discusses the effects of different implementation regarding to telemedicine by the internet of things. There are many ways of telemedicine which are implementing according to the available sources of Internet of things (IoT), the idea of this paper is to highlight those ways and discusses that how much these available procedures are useful for the remote areas. Comparison will be done as a conclusion of this paper

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.011
GPT teacher head0.281
Teacher spread0.269 · 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