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Record W1635797108

A patient based teaching module on the pharmacology of anesthetic drugs

2013· article· en· W1635797108 on OpenAlexaff
Jennifer J. Harris, Brent Herritt

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2013
Typearticle
Languageen
FieldHealth Professions
TopicSafe Handling of Antineoplastic Drugs
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAnestheticMedicineAnesthesiologyClinical pharmacologyMedical educationQuality (philosophy)PharmacologyAnesthesiaMedical physics
DOInot available

Abstract

fetched live from OpenAlex

Background Medical students who take electives in Anesthesiology often lack formal training in the pharmacology of the medications used by anesthesiologists. The clinical learning environment in the OR is often not conducive to comprehensive teaching opportunities, as patient care is the primary focus. Providing a teaching module on a portable device such as an iPad would facilitate a standardized learning approach for these students to learn about anesthetic drugs in the clinical OR setting. Objectives To develop a digital, interactive learning module on the pharmacology and clinical application of anesthetic drugs for medical students interested in anesthesia. Methods We compiled learning materials on the pharmacology of various classes of Anesthetic agents. We also designed virtual patient cases and coupled them with the background pharmacology. The resulting module was formatted on an iPad for ready access in the OR setting. Results The project will be completed throughout summer 2012; therefore results have not yet been obtained. The intention is to evaluate the effectiveness of this module for student learning either through a quality assurance questionnaire or formal testing of retained material once we have the module complete and functional.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.278
Teacher spread0.266 · 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 designSimulation or modeling
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

Citations0
Published2013
Admission routes1
Has abstractyes

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