Chicken Feet: A Model for Practising Locoregional Flaps of the Hand
Bibliographic record
Abstract
Background: The aim of this study was to determine the feasibility of the chicken foot model for surgical trainees interested in practising the designing, harvesting and inset of locoregional flaps of the hand. Methods: A descriptive study was performed to demonstrate the technical aspects of harvesting four locoregional flaps in a chicken foot model: fingertip volar V–Y advancement flap, four-flap and five-flap Z-plasty, cross-finger flap and first dorsal metacarpal artery (FDMA) flap. The study was performed in a surgical training laboratory on non-live chicken feet. No participants were involved in this study, apart from authors performing the descriptive techniques. Results: All flaps were successfully performed. Anatomical landmarks, soft tissue texture and flap harvest, as well as inset closely resembled clinical experience with patients. Maximal flap sizes were 12 × 9 mm for volar V–Y advancement, 5 mm limbs for Z-plasties, 22 × 15 mm for cross-finger flaps and 22 × 12 mm for FDMA flaps. The maximal webspace deepening with four-flap/five-flap Z-plasty was 20 mm and the FDMA pedicle length and diameter was 25 and 1 mm, respectively. Conclusions: Chicken feet can be effectively used as simulation models for hand surgical training with respect to gaining familiarity with the use of locoregional flaps of the hand. Further research requires testing for reliability and validity of the model on junior trainees.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".