Minimally Invasive Technique of Auricular Cartilage Harvest for Tissue Engineering
Bibliographic record
Abstract
Tissue engineered human cartilage is presently being utilized in clinical research programs in a variety of medical disciplines including otolaryngology, urology, and orthopedics. In this study, we present a new methodology for auricular cartilage harvest that can be applied to tissue engineering. Eight 16-week-old pigs were subjected to a traditional open cartilage harvest technique involving suture closure, while the other ear was subjected to the closed stitchless cartilage harvest, using a 12-gauge core biopsy needle. Surgical time was significantly (p < 0.0001) shorter (3.5 +/- 2.8 min for closed vs. 14.4 +/- 5 min for open), and no sutures where utilized in the closed technique. Sample weights were significantly (p < 0.00001) greater (0.115 +/- 0.028 g vs. 0.045 +/- 0.005 g) for the closed techniques. However, the minimally invasive closed technique had fewer incidents of bruising, hematoma, long-term stitch abscess, and scarring. Cell culture data shows no disadvantage to either technique with regards to cell growth characteristics. Final histological data from donor ears indicates favorable results with the minimally invasive technique. This technique preserves cell viability and isolation efficiency while decreasing surgical time and lessening postoperative complications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".