Simulation‐based clinical learning for the third year medical student: Effectiveness of transabdominal and transvaginal ultrasound for elucidation of OB/GYN scenarios
Bibliographic record
Abstract
PURPOSE: Point-of-care ultrasound (POCUS) is gaining recognition as a teaching modality that acts as an integrative learning tool during medical student transition to clinical rotations. This study aimed to determine if the use of ultrasound simulation enhances understanding of Obstetrical and Gynecological (Ob/Gyn) anatomy and pathology in third-year medical students (M3), and if M3 students found the simulator useful. METHODS: M3 students taking the OB/Gyn clerkship were invited to participate. Baseline knowledge of pelvic ultrasound anatomy and pathology was assessed with a multiple-choice question test. Participants received a one-hour OB/Gyn ultrasound simulation training session. A post-test assessed knowledge after the intervention. Survey data was collected regarding learning styles and learner satisfaction. RESULTS: Following simulator-based training, the median correct number of responses to the knowledge questions increased from 11 of 18 to 14 of 18 correct (P < .001). Statistically significant increases were also observed in comfort level with OB/GYN ultrasound (P < .001). All 68 students answered that the ultrasound simulator was helpful and enjoyed using the simulator. CONCLUSIONS: This study suggests that ultrasound simulators are useful for improvement in knowledge, comfort level, and ability to identify pathology in Ob/Gyn scenarios in M3 students.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".