Increasing Recognition of Fetal Heart Anatomy Using Online Tutorials and Mastery Learning Compared With Classroom Instructional Methods
Bibliographic record
Abstract
Assessment of the fetal heart is a challenging part of any routine obstetrical sonogram. The practice of teaching and learning this skill demands expertise in the visualization and interpretation of normal sonographic appearances of the fetal heart. Recognition of the pathological features associated with commonly seen congenital heart diseases is also very important. In the study reported here, students in an obstetrical sonography course were randomly assigned to two groups. The students in the control group (classroom instruction) on average assessed correctly all five anatomic cardiac features but determined the normal or abnormal status of only 19% of cases, 4.7of 25 ± 4.4 SD. The experimental group (online tutorials) correctly identified 39% of cases, 9.9 of 25 ± 2.7 SD, P < .01. The average score in the experimental group was greater than 84% of the students in the control group, a one sigma effect. These data support the conclusion that students learning to assess the fetal heart for the presence of congenital heart disease using a series of online tutorials and practice exercises compared with students receiving conventional classroom instruction demonstrated an improved ability to correctly identify normal and abnormal fetal heart structures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".