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Record W2005425354 · doi:10.1258/1357633054068919

Low-bandwidth telemedicine for pre- and postoperative evaluation in mobile surgical services

2005· article· en· W2005425354 on OpenAlexfundno aff
Edgar B. Rodas, Francisco Mora, Francisco Tamariz, Stephen Cone, Ronald C. Merrell

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
FundersMitacs
KeywordsTelemedicineMedicineMedical diagnosisMedical emergencySurgeryHealth careRadiology

Abstract

fetched live from OpenAlex

Low-bandwidth telemedicine was used for the pre- and postoperative evaluation of patients treated by a mobile surgery service in remote Ecuador. Realtime and store-and-forward telemedicine was employed, using PCs connected via the ordinary telephone network. Between February 2002 and July 2003, 144 patients were studied preoperatively and 50 postoperatively. It was possible to establish 20 satisfactory preoperative realtime connections, which allowed good-quality, simultaneous audiovisual transmission. Thus, there were 124 preoperative assessments done by store-and-forward telemedicine and 50 postoperative assessments. Diagnoses and management plans made by a surgeon using telemedicine were compared with those made independently by a second surgeon, who saw the patient face to face. Due to poor quality of the transmitted images, 43 patients were excluded from the preoperative study and 13 from the postoperative study. In the 101 preoperative evaluations, there was agreement in 78 cases (77%); in the 37 postoperative evaluations, there was agreement in 36 cases (97%). Telemedicine may reduce the time required on site for preoperative planning, and may provide reliable postoperative surveillance, thus improving the efficiency of mobile surgery services.

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)0.004
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.910
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.445
Teacher spread0.407 · 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 designOther design
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

Citations16
Published2005
Admission routes1
Has abstractyes

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