Randomized Controlled Trial: Acquisition of Basic Microsurgical Skills Through Smartphone Training Model
Bibliographic record
Abstract
Background: Microsurgery is essential in various surgical specialties, but learning these skills is challenging due to work hour limitations, patient safety concerns, documentation time, and ethical objections to practicing on live animals. This randomized controlled trial compares 2 microsurgical training models: the smartphone model and the microscope model. Methods: Thirty students without prior microsurgery experience were randomized into 3 groups: control (CG), smartphone (SG), and microscope (MG). Participants performed microsurgical skill tests and a chicken femoral artery anastomosis before and after 10 hours of standardized training according to their assigned models. The CG performed the test twice without training. Performance was assessed by time to complete the anastomosis, University of Western Ontario Microsurgery Skills Assessment scale, anastomosis patency, and time to complete the round-the-clock test. Results: No significant differences were observed among groups at baseline. Significant improvement in anastomosis time was achieved in the MG (27.4 minutes, P = 0.005) and SG (27.0 minutes, P = 0.005), but not in the CG (13.1 minutes, P = 0.161). On the University of Western Ontario scale, the MG improved by 6.0 points (P = 0.002), the SG by 5.1 points (P = 0.006), and the CG by 2.4 points (P = 0.009). Patency rate significantly improved in the MG and SG (P = 0.002) but not the CG (P = 0.264). Round-the-clock time improved in all groups (P < 0.001). Conclusions: Basic microsurgical skills can be effectively learned using the smartphone training model, with performance improvements comparable to the microscope model. Its main limitation is the lack of stereoscopy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".