Back to Anatomy: Improving Landmarking Accuracy of Clinical Procedures Using a Novel Approach to Procedural Teaching
Bibliographic record
Abstract
OBJECTIVES: Many believe that knowledge of anatomy is essential for performing clinical procedures; however, unlike their surgical counterparts, internal medicine (IM) programs rarely incorporate anatomy review into procedural teaching. This study tested the hypothesis that an educational intervention focused on teaching relevant surface and underlying anatomy would result in improved bone marrow procedure landmarking accuracy. METHODS: This was a preintervention-postintervention prospective study on landmarking accuracy of consenting IM residents attending their mandatory academic half-day. The intervention included an interactive video and visualization exercise; the video was developed specifically to teach the relevant underlying anatomy and includes views of live volunteers, cadavers, and skeletons. RESULTS: Thirty-one IM residents participated. At pretest, 48% (15/31) of residents landmarked accurately. Inaccuracy of pretest landmarking varied widely (n = 16, mean 20.06 mm; standard deviation 30.03 mm). At posttest, 74% (23/31) of residents accurately performed the procedure. McNemar test revealed a nonsignificant trend toward increased performance at posttest (P = 0.076; unadjusted odds for discordant pairs 3; 95% confidence interval 0.97-9.3). The Wilcoxon signed rank test demonstrated a significant difference between pre- and posttest accuracy in the 16 residents who were inaccurate at pretest (P = 0.004). No association was detected between participant baseline characteristics and pretest accuracy. CONCLUSIONS: This study demonstrates that residents who were initially inaccurate were able to significantly improve their landmarking skills by interacting with an educational tool emphasizing the relation between the surface and underlying anatomy. Our results support the use of basic anatomy in teaching bone marrow procedures. Results also support the proper use of video as an effective means for incorporating anatomy teaching around procedural skills.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".