CyberPatient<sup>TM</sup>—An Innovative Approach to Medical Education
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.
Bibliographic record
Abstract
Background: Variety of tools has been used to teach history-taking skills to novice learners. Standardized Patient (SP) is the gold standard for medical education. We hypothesized that the use of online simulation platforms CyberPatientTM (CP) is as effective as SP. Methods: In this prospective randomized controlled trial study, the educational effectiveness of CP was compared to SP in improving history taking skills. Twenty-two incoming students at University of British Columbia (UBC) were randomly divided in to two (SP and CP) groups. SP Group (n = 11) practiced their history taking skills with the standardized patients and CP Group (n = 11)—with CyberPatients. The content for both groups included 3 cases of GI pathology and the study time was 60 minutes. Assessment method included Objective Structured Clinical Examination (OSCE) before and after interventions. Data were analysed in a two-way between/within ANOVA and Wald test was used to deal with the violation of the ANOVA assumptions. Economic benefits were assessed as Cost-effectiveness (calculated as Cost/Effect Ratio) and Cost-Value Proposition (Cost-Vale Relationship). Results: Results of this study indicated that both groups had significant (SP group p = 0.006 and CP group p = 0.0001) improvement in the knowledge domain of history taking. The history taking knowledge variable in both groups manifested a significant main effect of time indicating that students did better after interventions, F (1, 15.1) = 10.5, p = 0.011. The groups performed at a similar level after intervention. Moreover, results show that the use of the CP is more cost-effective and has a better cost/value proposition for medical education. Conclusion: We conclude that CyberPatientTM is as effective as using standardized patients in delivery of practical knowledge for novice medical students, however, CyberPatientTM is more economically rewarding.
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 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 | 0.001 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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