Evaluating the knowledge acquisition of lower limb anatomy among medical students during the post‐acute COVID‐19 era
Bibliographic record
Abstract
Anatomy is the foundation of many medical and surgical specialties yet knowledge acquisition and retention among medical students is questionable. Over the years the anatomy teaching environment and teaching modalities have changed, even more so with the onset of the COVID-19 pandemic and the shift to a virtual environment. The aim of this study was to evaluate the knowledge acquisition of applied musculoskeletal lower limb clinical anatomy among first year medical students in Malta following the transition back to face-to-face lectures. The Kahoot online game-based quiz platform was used through a best out of four multiple-choice setting across four sessions. Scores generated by the platform along with frequencies of correctly answered questions were utilized to measure knowledge acquisition. The average scores for each question across sessions were statistically analyzed using ANOVA and student's t-test accordingly. Across the four sessions, the positive percentage response for clinical based questions remained higher than for pure anatomy questions. Anatomy knowledge acquisition appears to be subjective to clinical based knowledge rather than pure anatomy. There may be a plethora of reasons as to this outcome including the misconception that anatomy is not essential for clinical practice as well as the potential aftermath of the COVID-induced virtual learning environment. Further research is merit to ensure that students are provided with the best tools to enhance their knowledge acquisition, both as students and as future doctors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".