Cognitive task analysis, kinesiology and mental imagery: Challenging surgical attrition
Bibliographic record
Abstract
Abstract Introduction: We verified the value of cognitive task analysis and kinesiology in the teaching of basic surgical skills. Furthermore we wanted to identify the role of mental imagery in the acquisition and retention of surgical skills particularly for an emergency procedure seldom done in routine practice (cricothyroidotomy). Methods: We randomly divided 44 first year medical school students in three groups. The first group (ATLS) was taught to perform a cricothyroidotomy according to the ATLS protocol. Group 2 (kinesiology, KG) learned the same procedure but this time following cognitive task analysis and with kinesiology principles (8 simple movements and specific posture correction). Finally in group 3 (KG + MI), we used the same kinesiology principles as in group 2 but we also asked the students to use mental imagery (MI) daily and rapidly briefed them on the topic. Two weeks after the teaching session, an OSCE (25 marks) verified the acquisition of the skills, and 6 and 12 months later we evaluated attrition (loss of skills). Results: ∗ . 2 weeks 6 months 12 months KG + MI 20.3 ± 1.5 (n = 15) ∗ 19.7 ± 2.4 (n = 14) ∗ 21.5 ± 1.8 (n = 15) ∗ KG 19.3 ± 2.9 (n = 13) 19.4 ± 3.4 (n = 13) 19.5 ± 1.9 (n = 13) ∗ ATLS 18.2 ± 2.5 (n = 16) ∗ 16.6 ± 4.8 ∗ (n = 15) 16.5 ± 2.9 (n = 16) ∗ ∗ p Conclusions: Kinesiology-guided teaching following cognitive tasks analysis seems to improve the maintenance of surgical skills over traditional ATLS technique as showed by the 12 months results. The addition of mental imagery statistically improved acquisition and maintenance of skill better than both the ATLS group and the KG group. This confirms the potentially prominent role that these alternative educational approaches will play in the future of surgical 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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".