Action observation for sensorimotor learning in surgery
Bibliographic record
Abstract
BACKGROUND: Acquiring new motor skills to learn complex movements and master the use of a diverse range of instruments is fundamental for developing expertise in surgery. Although aspects of skill development occur through trial and error, watching the performance of another individual (action observation) is an increasingly important adjunct for the acquisition of these complex skills before performing a procedure. The aim of this review was to examine the evidence in support of the use of action observation in surgery. METHODS: A narrative review of observational learning for surgical motor skills was undertaken. Searches of PubMed and PsycINFO databases were performed using the terms 'observational learning' OR 'action observation' AND 'motor learning' OR 'skill learning'. RESULTS: Factors such as the structure of physical practice, the skill level of the demonstrator and the use of feedback were all found to be important moderators of the effectiveness of observational learning. In particular, observation of both expert and novice performance, cueing attention to key features of the task, and watching the eye movements of expert surgeons were all found to enhance the effectiveness of observation. It was unclear, however, whether repeated observations were beneficial for skill learning. The evidence suggests that these methods can be employed to enhance surgical training curricula. CONCLUSION: Observational learning is an effective method for learning surgical skills. An improved understanding of observational learning may further inform the refinement and use of these methods in contemporary surgical training curricula.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".