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Record W4386910584 · doi:10.1007/s11701-023-01716-6

Robotic assisted surgery in the United Arab Emirates: healthcare experts’ perceptions

2023· article· en· W4386910584 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Robotic Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInterviewPerceptionHealth careHealthcare deliveryMiddle EastMedical educationNursingFamily medicinePsychology

Abstract

fetched live from OpenAlex

The adoption of Robotic Assisted Surgery (RAS) has grown around the world. This is also the case in the Middle East and Gulf region and specifically to the United Arab Emirates (UAE). The perception of RAS has been studied in the USA, Europe, and Canada. However, there is limited research on the perception of RAS in the UAE. The study aims to examine the perception of RAS among healthcare experts in the UAE and potential challenges. This qualitative study is based on interviewing healthcare experts in the UAE. Most of the study participants were clinicians and surgeons. In the UAE, RAS is adopted in general surgery, urology, brain surgery, and obstetrics and gynecology. Our findings show that healthcare experts have positive perceptions of RAS. The cost and lack of RAS training program are considered as challenges to adopting RAS in healthcare practices. More research is encouraged to examine perception variations with surgical practices in the UAE, Gulf and the Middle East.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
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 head0.161
GPT teacher head0.360
Teacher spread0.199 · 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