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Record W2065845227 · doi:10.1177/1357633x14552385

Clinical applications of videoconferencing: a scoping review of the literature for the period 2002–2012

2014· review· en· W2065845227 on OpenAlexaboutno aff
Farhad Fatehi, Nigel R Armfield, Mila Dimitrijevic, Len Gray

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2014
Typereview
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsVideoconferencingMedicineInclusion (mineral)MEDLINEClinical trialIntervention (counseling)Family medicineNursingPsychologyMultimediaComputer science

Abstract

fetched live from OpenAlex

We conducted a scoping review of the literature on the clinical applications of videoconferencing. Electronic searches were performed using the PubMed, Embase and CINHAL databases to retrieve papers published from 2002 to 2012 that described clinical applications of videoconferencing. The initial search yielded 4923 records and after removing the duplicates and screening at title/abstract level, 505 articles met the inclusion criteria and were reviewed at full-text level. The countries with the highest number of papers were the US, Australia and Canada. Most studies were non-randomised controlled trials. The discipline with highest number of published studies (39%) was mental health, followed by surgery (7%) and general medicine (6%). The type of care delivered via video comprised acute, sub-acute and chronic care, but in 44% of the papers, the intervention was used for a combination of these purposes. Videoconferencing was used for all age groups but more frequently for adults (20%). Most of the papers (91%) reported using videoconferencing for several clinical purposes including management, diagnosis, counselling and monitoring. The review showed that videoconferencing has been used in a wide range of disciplines and settings for different clinical purposes. The practical value of published papers would be improved by following standard guidelines for reporting research projects and clinical trials.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.665
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.084
GPT teacher head0.476
Teacher spread0.392 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations50
Published2014
Admission routes1
Has abstractyes

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