Bridging the gap: Using 3D printed polycaprolactone implants to reconstruct circumferential tracheal defects in rabbits
Bibliographic record
Abstract
OBJECTIVE: 1) To assess the feasibility of reconstructing 2-cm-long circumferential tracheal defects with a 3D printed polycaprolactone (PCL) implant in rabbits. 2) To evaluate endoscopic, histologic, and functional characteristics of a PCL tracheal implant over time. METHODS: Ten New Zealand rabbits were included in this study. A 2-cm-long 3D printed PCL tracheal implant was created. All rabbits underwent surgical excision of a 2-cm-long cm segment of cervical trachea, which was reconstructed with the implant. Rabbits were sacrificed at the following time points: 0, 4, 5, 6, and 7 weeks postoperatively. At these time points, a rigid bronchoscopy was performed, and blinded evaluators calculated the percentage of airway stenosis. The tracheas were then harvested and prepared for histologic analysis. RESULTS: All rabbits survived to their date of sacrifice except for one. Rabbits were euthanized between 0 to 54 days postoperatively with a median of 30 days. All rabbits developed significant granulation tissue with an average percentage stenosis of 92.3% ± 6.1%. On histology, granulation was present with extensive neovascularization and mixed inflammatory cells. There was re-epithelialization present on the luminal surface of the PCL implant near the anastomoses but absent at the center of the implant. CONCLUSION: This study demonstrates that our 2-cm-long 3D printed PCL tracheal implant can be used to reconstruct a tracheal defect of equivalent size in a New Zealand rabbit model in the short term. However, significant granulation tissue formation limits long-term survival. Further research is warranted to limit the granulation tissue overgrowth. LEVEL OF EVIDENCE: NA Laryngoscope, 2019.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".