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Record W2327320798 · doi:10.1097/aco.0000000000000117

Robotics and regional anesthesia

2014· review· en· W2327320798 on OpenAlex
Mohamad Wehbe, Marilù Giacalone, Thomas M. Hemmerling

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

VenueCurrent Opinion in Anaesthesiology · 2014
Typereview
Languageen
FieldMedicine
TopicMinimally Invasive Surgical Techniques
Canadian institutionsMontreal General HospitalMcGill University
Fundersnot available
KeywordsRobotMedicineWorkloadRoboticsRegional anesthesiaArtificial intelligenceAnesthesiaHuman–computer interactionComputer science

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Robots in regional anesthesia are used as a tool to automate the performance of regional techniques reducing the anesthesiologist's workload and improving patient care. The purpose of this review is to show the latest findings in robotic regional anesthesia. RECENT FINDINGS: The literature separates robots in anesthesia into two groups: pharmacological robots and manual robots. Pharmacological robots are mainly closed-loop systems that help in the titration of anesthetic drugs to patients undergoing surgery. Manual robots are mechanical robots that are used to support or replace the manual gestures performed by anesthesiologists. Although in the last decade researchers have focused on the development of decision support systems and closed-loop systems, more recent evidence supports the concept that robots can also be useful in performing regional anesthesia techniques. SUMMARY: Robots can improve the performance and safety in regional anesthesia. In this review, we present the developments made in robotic and automated regional anesthesia, and discuss the current state of research in this field.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.178
GPT teacher head0.427
Teacher spread0.249 · 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