An Innovative Technique of Microsurgical Training on Fresh “Chicken Quarter” Model: Our Experience
Bibliographic record
Abstract
Abstract Purpose Regular practice, quality clinical exposure, and academic discussion are essential in any surgical specialty training. This study discusses and validates the option of using a fresh “chicken quarter” model with a measurable scoring system, as a standard training regimen in microvascular surgery. This can be a very effective, economical, and easily accessible model for residents. Materials and Methods This study was conducted in the Department of Plastic surgery, from October 2020 to May 2021. Twenty-four fresh “chicken quarter” specimens were dissected and the ischial arteries and femoral veins' external diameter (ED) were measured. The microsurgical skills of the trainee were assessed in 6 months intervals using the Objective Structured Assessment of Technical Skills Scale (OSATS) as well as the time taken for anastomosis. All the data were analyzed using SPSS (statistical package for social sciences) version 21. Results A task-specific score value of 50% on October 2020 improved to 85.7% by May 2021. This was found to be statistically significant (p = 0.043). The mean ED of the ischial artery and femoral vein was 2.07 and 2.26 mm, respectively. The mean width of the vein measured at the lower one-third of the tibia was 2.08 mm. A greater than 50% reduction in anastomosis time was observed after a period of 6 months. Conclusion In our minimal experience, the “chicken quarter model” with OSATS scoring system seems to be effective, economical, very affordable, and easily accessible microsurgery training model for the residents. Our study is done only as a pilot project due to limited resources and we have the plan to introduce it as a proper training method in the near future with more residents.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".