Cognitive Skills Analysis, Kinesiology, and Mental Imagery in the Acquisition of Surgical Skills
Bibliographic record
Abstract
GOAL: Isolate and evaluate the impact of mental imagery on the acquisition of an emergency surgical technique. METHOD: We studied 44 first-year medical students performing a cricothyrotomy on a mannequin to determine the impact of teaching using mental imagery (MI) and/or kinesiology (KG) compared to the standard Advandec Trauma Life Support (ATLS) approach. Students were randomly assigned to one of three groups: MI and KG, KG alone or control (ATLS). Two weeks after the one-hour teaching session, they were evaluated with an OSCE testing the performance of the different steps of the technique, the time required and its fluidity. RESULTS: Total results (maximum: 25 marks) are as follows: KG + MI = 20.3 +/- 1.5 ; KG = 19.3 +/- 2.9 ; ATLS = 18.2 +/- 2.5. The only statistically significant difference for total results was in the use of MI and KG compared to the control group. Kinesiology alone or with mental imagery improved the fluidity of the performance. CONCLUSION: Many factors influence the acquisition of a surgical technique. This study showed that acquisition and performance of an emergency procedure (cricothyrotomy) was improved when mental imagery and kinesiology were combined to teach it.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".