Identification of Gaps in the Achievement of Undergraduate Anesthesia Educational Objectives Using High-Fidelity Patient Simulation
Bibliographic record
Abstract
UNLABELLED: In this study we sought to identify educational gaps in medical students' knowledge using human patient simulation. The Undergraduate Committee developed 10 scenarios based on anesthesia curriculum objectives. Checklists were designed by asking 15 faculty members involved in undergraduate education to propose expected performance items at a level appropriate for medical students. These items consisted of essential performance items as well as critical management omissions. Checklists were used to score students' videotaped performances. Checklist items common to more than one scenario were grouped for data analysis and identification of gaps in achievement of educational objectives. Eighteen groupings of expected performance criteria and 8 groupings of critical management omissions were established. Performance data of 165 students were analyzed. Common management omissions were lack of adequate airway management, failure to check blood pressure, and failure to stop the anesthetic. Students reliably performed defibrillation, notation of vital signs, auscultation of lung fields, and administration of IV fluids. The most common critical omissions were failing to a). call for help, b). take a history/do physical examination, and c). prepare airway equipment. Management and critical omissions noted during performance assessments provide information regarding students' educational needs, enabling faculty to focus attention on demonstrated areas of weakness. IMPLICATIONS: This study involved the use of high-fidelity patient simulation that offers standardized clinical experiences that can detect gaps in medical students' knowledge base and clinical performance. This information can be used by faculty to focus their teaching efforts to ensure competency in important educational areas.
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 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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".