Improvement and Retention of Arthroscopic Skills in Novice Subjects Using Fundamentals of Arthroscopic Surgery Training (FAST) Module
Bibliographic record
Abstract
INTRODUCTION: Analysis of the Fundamentals of Arthroscopy Surgery Training (FAST) workstation regarding increased proficiency and retention of basic arthroscopy skills in novice subjects. METHODS: First-year medical students from a single allopathic medical school performed weekly standardized FAST workstation modules for a consecutive 6 weeks. Primary outcomes evaluated were time to task completion and error rate on specific modules. Scores were analyzed using a one-way repeated measures analysis of variance design for overall trends in time and errors over the 6-week study. Psychomotor retention was analyzed after a 12-week and 24-week interlude. RESULTS: Across the initial 6-week study, the average time to complete all modules at the workstation decreased significantly (P < 0.001) with a mean reduction in the total workstation time of 21.9 minutes (s = 8.12 minutes). Weekly comparisons showed the most significant improvement from week 1 to week 2 for the total workstation time (P < 0.001). Results after a 12-week and 24-week interval of inactivity demonstrated no significant difference in the mean workstation time or errors when compared with the original 6-week study. DISCUSSION: The FAST workstation significantly improved the task performance of novice participants over a 6-week period with no significant deterioration in task performance after 12 and 24 weeks of inactivity.
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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 | 0.001 |
| 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) | 0.003 | 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".