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Record W4390819365 · doi:10.1213/ane.0000000000006835

Robotic Anesthesia: A Vision for 2050

2024· article· en· W4390819365 on OpenAlexaff
Thomas M. Hemmerling, Sean D. Jeffries

Bibliographic record

VenueAnesthesia & Analgesia · 2024
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsSedationAnesthesiaMedicineIntubationRobotVariety (cybernetics)Drug administrationAnestheticIntensive care medicineComputer scienceArtificial intelligencePharmacology

Abstract

fetched live from OpenAlex

The last 2 decades have brought important developments in anesthetic technology, including robotic anesthesia. Anesthesiologists titrate the administration of pharmacological agents to the patients' physiology and the needs of surgery, using a variety of sophisticated equipment (we use the term "pilots of the human biosphere"). In anesthesia, increased safety seems coupled with increased technology and innovation. This article gives an overview of the technological developments over the past decades, both in terms of pharmacological and mechanical robots, which have laid the groundwork for robotic anesthesia: target-controlled drug infusion systems, closed-loop administration of anesthesia and sedation, mechanical robots for intubation, and the latest development in the world of communication with the arrival of artificial intelligence (AI)-derived chatbots are presented.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.022
GPT teacher head0.310
Teacher spread0.289 · 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.

Study designNot applicable
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

Citations22
Published2024
Admission routes1
Has abstractyes

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