A Method for Functional Task Alignment Analysis of an Arthrocentesis Simulator
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Bibliographic record
Abstract
INTRODUCTION: During simulation-based education, simulators are subjected to procedures composed of a variety of tasks and processes. Simulators should functionally represent a patient in response to the physical action of these tasks. The aim of this work was to describe a method for determining whether a simulator does or does not have sufficient functional task alignment (FTA) to be used in a simulation. METHODS: Potential performance checklist items were gathered from published arthrocentesis guidelines and aggregated into a performance checklist using Lawshe's method. An expert panel used this performance checklist and an FTA analysis questionnaire to evaluate a simulator's ability to respond to the physical actions required by the performance checklist. RESULTS: Thirteen items, from a pool of 39, were included on the performance checklist. Experts had mixed reviews of the simulator's FTA and its suitability for use in simulation. Unexpectedly, some positive FTA was found for several tasks where the simulator lacked functionality. CONCLUSIONS: By developing a detailed list of specific tasks required to complete a clinical procedure, and surveying experts on the simulator's response to those actions, educators can gain insight into the simulator's clinical accuracy and suitability. Unexpected of positive FTA ratings of function deficits suggest that further revision of the survey method is required.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it