Evaluation of domestic and Yucatan swine nasal sinus anatomy as models for future sinonasal research of medications delivered by standard instruments used in functional endoscopic sinus surgery
Bibliographic record
Abstract
BACKGROUND: There is a need to find an animal model to study new medications to improve mucosal wound healing after functional endoscopic sinus surgery (FESS). Current literature suggests swine as a potential candidate. The lack of information correlating swine computer tomography (CT) and endoscopic sinonasal anatomy prompted us to investigate them in the domestic and Yucatan swine to determine their feasibility as models to test new medications and drug-embedded stents applied using FESS techniques. METHODS: Two domestic pig heads and 2 Yucatan pig heads were imaged using helical thin slice (1 mm) CT. Two rhinologists analyzed the images and performed endoscopy on the swine. Particular attention was given to accessing the frontal sinus and suturing stents to the nasal septum using standard endoscopic instruments. RESULTS: CT confirmed that swine sinonasal anatomy is largely similar to human, with all major sinuses present. The middle and inferior turbinates of swine arise from a single uniturbinate. The superior turbinates contain large concha bullosa. Unlike human, swine nasal septum is bone anteriorly and cartilage posteriorly. The frontal sinus ostia, regardless of head size, were consistently around 10 cm from the nasal aperture. On endoscopy, domestic swine frontal sinus ostia were easily accessible for topical medication deposition. Silastic splints can be sutured to the domestic swine septum through the posterior cartilaginous portion, allowing for studies involving medication-eluting material. The narrower nasal cavity of Yucatan pigs prohibited endoscopic maneuvers. CONCLUSION: Domestic swine, but not Yucatan, are a feasible model for future sinonasal research using standard FESS instruments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".