Teaching Digital Rectal Examinations to Medical Students
Bibliographic record
Abstract
PURPOSE: The digital rectal examination (DRE) is a necessary part of a complete physical examination and evaluation of a patient, yet teaching of this examination to medical students is often inadequate. This study was a comparative evaluation of the effectiveness of the rectal teaching associate (RTA), lecture, role-playing, and simulated models as methods for teaching the DRE procedure to undergraduate medical students at Memorial University of Newfoundland Faculty of Medicine. METHOD: A total of 65 third-year medical students were randomly assigned to either an experimental or control group. Both groups received a lecture and practiced the DRE on a simulated model. The experimental group received further training from an RTA. Students completed a pre- and post-intervention knowledge assessment, an objective structured clinical exam (OSCE) measuring performance of the DRE, and a satisfaction survey. RESULTS: Mean knowledge scores increased significantly for both groups (18.73 to 22.32, p <.0001). The control group scored significantly higher on the post-intervention assessment than did the experimental group (23.11 versus 21.47, p =.025) The experimental group scored higher on the OSCE (27.52 versus 23.80, p =.001) and rated the RTA as a more effective method for learning the DRE. CONCLUSIONS: This first study using RTAs to teach the DRE as a global skill for evaluating the rectum suggests that the RTA method is effective for increasing skills and students' confidence in the procedure.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".