Examination of Learning Trajectories for Simulated Lumbar Puncture Training Using Hand Motion Analysis
Bibliographic record
Abstract
OBJECTIVES: A prospective cohort study was conducted using hand motion analysis (HMA) to assess the acquisition and retention of technical proficiency among first-year medical students learning the lumbar puncture (LP) skill in a simulated setting. METHODS: Twenty-five subjects attended three or four simulation sessions at 6-week intervals. The initial session consisted of an introduction to the procedure and a baseline HMA assessment. This was followed by a session involving deliberate practice and evaluation using HMA. Subject HMA metrics were followed over time and compared to performance benchmarks to determine the volume of practice required to achieve and maintain technical proficiency in the simulated setting. Performance benchmarks were derived from the assessment of experts using HMA. RESULTS: Subject baseline metrics were significantly different from expert (p < 0.01). At the outset of session 2, none of the subjects achieved the performance benchmarks. At the outset of sessions 3 and 4, 40 and 60% of subjects, respectively, demonstrated retention of technical proficiency. However, there was evidence of significant skill erosion between sessions (p < 0.01). The mean number of practice attempts required to achieve technical proficiency declined between sessions. Regression analysis indicated that there was a significant training effect for all students (overall negative slopes) over time. When examining the group as a whole, the speed at which students reached the expert benchmark was not significantly associated with number of practices in the first three sessions, although for some individuals these factors did appear associated. A total of 76% of subjects retained technical proficiency by session 4 and required a mean of 14 practices (range = 5 to 19). CONCLUSIONS: These results show that the majority of students require three to four sessions of deliberate practice to achieve a sustainable level of proficiency in the LP procedure. There is considerable variation in learning progression and retention of technical proficiency. These results have important implications for the design and resource requirements of a competency-based medical education program targeting LP training.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".