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FAER … and Technology

2018· article· en· W2807812688 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

VenueASA Monitor · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicTwentieth Century Scientific Developments
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

James C. Eisenach, M.D., is F.M. James III Professor of Anesthesiology and Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.Anesthesiologists are immersed in technology. We rely on technology to help us perform procedures, provide anesthesia, monitor for safety, and predict which of our patients deserves unusual or special treatments. The Foundation for Anesthesia Education and Research (FAER) provides career development to young physicians whose research will transform the specialty, including in technology. Oftentimes this transformation doesn’t occur for decades from the fundamental discovery to the application, and by the time we see the latest oximetry, capnography, ultrasound, simulator, smart notifications, etc., we have long forgotten, if we were even aware, of how FAER-funded investigators helped made this happen. There is another group of pioneers involved nearer to the time of technology breakthroughs – the innovative entrepreneurs in the specialty – and FAER is now reaching out to help them achieve their goals. Ted Stanley, M.D., a pioneer in pharmacology and technology innovation and former FAER Board member, felt that these individuals more than anything need networking opportunities with other inventors and with the groups which fund them. In the past year FAER has sponsored such opportunities at two national meetings of anesthesiologists in Shark Tank®-like sessions where they present their nearly-ready for prime time ideas and discuss hurdles to market implementation. Here are just a few ideas which FAER helped disseminate at these sessions, some of which might soon appear in your practice: A patient monitoring system for hospitalized patients which records respiratory and heart rate, oxygen saturation and perfusion, without any sensors, wires, or the need for patient cooperation (Atapir, Arthur Wallace, M.D., UCSF).A video laryngoscope which includes a warning system to help avoid pharyngeal trauma that can occur when the operator shifts attention from the laryngoscope to the video screen (Clearsight – Barrett Larson, M.D., Stanford).A simple device to alert and prevent a common problem inside and outside the operative room – I.V. bags running dry (FIVA – Orlando Hung, M.D., Dalhousie University).A propofol prodrug without pain on injection and with near instantaneous conversion to propofol following injection (Accellient – Bill Kerns, D.V.M.).An external fetal heart rate monitor which doubles as a fetal pulse oximeter, providing additional information to obstetrician and anesthesiologist about fetal well-being (Raydiant Oximetry, Neil Ray, M.D., and Mark Rosen, M.D., UCSF).A novel intra-venous anesthetic working on GABA receptors but without hemodynamic depression or adrenal suppression (Ed Bertaccini, M.D., Stanford).A new type of ventilator for patients in acute respiratory failure which uses complex mixing of simultaneous frequencies of volume oscillations to more evenly distribute ventilation in diseased lungs (OscillaVent – David Kaczka, M.D., Ph.D., University of Iowa).FAER and the ASA neither endorse nor invest in these ideas. But thanks in large part to Dr. Stanley, FAER is pleased to encourage the development of innovation aimed to support these anesthesiologists who will change the practice of medicine in our specialty. Please stop by and see more new ideas during the FAER Swimming with Sharks session on Saturday, October 13, from 1-3 p.m. at ANESTHESIOLOGY® 2018!

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.221
Teacher spread0.206 · 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