Cadaveric Dissection in Relation to Problem‐Based Learning Case Sequencing: A Report of Medical Student Musculoskeletal Examination Performances and Self‐Confidence
Bibliographic record
Abstract
Mercer University School of Medicine utilizes a problem-based learning (PBL) curriculum for educating medical students in the basic clinical sciences. In 2014, an adjustment was piloted that enabled PBL cases to align with their corresponding cadaver dissection that reviewed the content of anatomy contained in the PBL cases. Faculty had the option of giving PBL cases in sequence with the cadaveric dissection schedule (sequential group) or maintaining PBL cases out of sequence with dissections (traditional group). During this adjustment, students' academic performances were compared. Students' perception of their own preparedness for cadaveric dissection, their perceived utility of the cadaver dissections, and free-response comments were solicited via an online survey. There were no statistically significant differences when comparing student mean examination score values between the sequential and traditional groups on both multidisciplinary examinations (79.39 ± 7.63 vs. 79.88 ± 7.31, P = 0.738) and gross anatomy questions alone (78.15 ± 10.31 vs. 79.98 ± 9.31, P = 0.314). A statistically significant difference was found between the sequential group's and traditional group's (63% vs. 29%; P = 0.005) self-perceived preparedness for cadaveric dissections in the 2017 class. Analysis of free-response comments found that students in the traditional group believed their performance in PBL group, participation in PBL group and examination performance was adversely affected when compared to students with the sequential schedule. This study provides evidence that cadaveric dissections scheduled in sequence with PBL cases can lead to increased student self-confidence with learning anatomy but may not lead to improved examination scores.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".