A patient based teaching module on the pharmacology of anesthetic drugs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.019 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".