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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".