Application of Motor Learning Principles to Complex Surgical Tasks: Searching for the Optimal Practice Schedule
Bibliographic record
Abstract
Practice of complex tasks can be scheduled in several ways: as whole-task practice or as practice of the individual skills composing the task in either a blocked or a random order. The authors used those 3 schedules to study 18 participants' learning of an orthopedic surgical task. They assessed learning by obtaining expert evaluation of performance and objective kinematic measures before, immediately after, and 1 week after practice (transfer test). During acquisition, the blocked group showed superior performance for simple skills but not for more complex skills. For the expert-based measures of performance, all groups improved from pretest to posttest and remained constant from posttest to transfer. Measures of the final product showed that the whole-practice group's outcomes were significantly better than those of the random group on transfer. All groups showed better efficiency of motions in the posttest than in the pretest. Those measures were also poorer on the transfer test than on the posttest. The present evidence does not support the contextual interference effect--hypothetically, because of the inherent cognitive effort effect associated with some of the component skills. The authors recommend that surgical tasks composed of several discrete skills be practiced as a whole. The results of this study demonstrate the importance of critically appraising basic theories in applied environments.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".