Corrosion casting of the subglottis following endotracheal tube intubation injury: A pilot study in Yorkshire piglets
Bibliographic record
Abstract
PURPOSE: Subglottic stenosis can result from endotracheal tube injury. The mechanism by which this occurs, however, is not well understood. The purpose of this study was to examine the role of angiogenesis, hypoxia and ischemia in subglottic mucosal injury following endotracheal intubation. METHODS: Six Yorkshire piglets were randomized to either a control group (N=3, ventilated through laryngeal mask airway for corrosion casting) or accelerated subglottic injury group through intubation and induced hypoxia as per a previously described model (N=3). The vasculature of all animals was injected with liquid methyl methacrylate. After polymerization, the surrounding tissue was corroded with potassium hydroxide. The subglottic region was evaluated using scanning electron microscopy looking for angiogenic and hypoxic or degenerative features and groups were compared using Mann-Whitney tests and Friedman's 2-way ANOVA. RESULTS: Animals in the accelerated subglottic injury group had less overall angiogenic features (P=.002) and more overall hypoxic/degenerative features (P=.000) compared with controls. Amongst angiogenic features, there was decreased budding (P=.000) and a trend toward decreased sprouting (P=.037) in the accelerated subglottic injury group with an increase in intussusception (P=.004), possibly representing early attempts at rapid revascularization. Amongst hypoxic/degenerative features, extravasation was the only feature that was significantly higher in the accelerated subglottic injury group (P=.000). CONCLUSIONS: Subglottic injury due to intubation and hypoxia may lead to decreased angiogenesis and increased blood vessel damage resulting in extravasation of fluid and a decreased propensity toward wound healing in this animal model.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".