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Record W2050833367 · doi:10.1007/s00268-007-9076-5

Telesurgery: Remote Knowledge Translation in Clinical Surgery

2007· article· en· W2050833367 on OpenAlex
Mehran Anvari

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWorld Journal of Surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineVascular surgeryThe InternetTelemedicineInvasive surgeryRobotic surgeryAbdominal surgeryMedical educationSurgeryCardiac surgeryComputer scienceHealth careWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

Dissemination of new surgical knowledge, skills, and techniques across the wide spectrum of practicing surgeons in the community is often difficult and slow. This is even more problematic in countries such as Canada, where geographic distances separate a large portion of community surgeons from the large teaching centers. As an example, the penetration of advanced minimally invasive techniques in Canada has been severely hampered by the inability to provide adequate training opportunities and support for community surgeons, many of whom live in remote regions of the country. In an attempt to overcome the barriers that exist, the Centre for Minimal Access Surgery (CMAS) at McMaster University has been using broadband Internet and telecommunication systems to provide distance training and mentoring to community surgeons living in remote northern communities of Canada. This article describes our experience with telementoring and robot-assisted remote telepresence surgery and assisting, between a teaching hospital in Hamilton and two community hospitals in northern Ontario and Quebec.

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)0.014
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
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 head0.230
GPT teacher head0.412
Teacher spread0.182 · 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