A New Approach to Microsurgical Medical Education: Mobile Phone Repairs
Bibliographic record
Abstract
Background: Technology has been increasingly used as a teaching tool in medical education, and simulation training is at the forefront of that shift in teaching methods.This project proposes an innovative tool for training students in microsurgery.Through fixing their cell phones under the guidance of a supervisor, students got a chance to improve their technique in microscope use.Furthermore, the procedure allowed for students to discuss insecurities surrounding the performance of microsurgery. Materials and Methods:Eight students from the Federal University of São Paulo were involved in the project and were selected through a phone screening process.Those students brought in their own faulty cell phones which were the main materials used.Other materials included pieces that needed replacing in the phones and the tools needed to open, close and handle the apparatus generally.Results: 87.5% of the students achieved the expected result with a successful procedure and 25% of students had technical problems during the procedure.75% of students, however, had no major technical problems during the procedure.Two of the eight students who participated in this project had trouble with the cell phones one month after the procedure.The remaining had fully functional devices. Conclusion:The majority of students successfully completed the cell phone repairs.Overall, there was a high level of acceptance of the project by students as a successful learning tool which increased both their abilities and self-confidence.Students overall left satisfied with the activity and said they would recommend it to others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".