Establishing the Ideal Conditions to Create an Airway Fire Using a Porcine Airway Model
Bibliographic record
Abstract
Abstract Objective Airway fires are a rare but devastating complication of airway surgery. Although protocols for managing airway fires have been discussed, the ideal conditions for igniting airway fires remain unclear. This study examined the oxygen level required to ignite a fire during a tracheostomy. Study Design Porcine Model. Setting Laboratory. Methods Porcine tracheas were intubated with a 7.5 air‐filled polyvinyl endotracheal tube. A tracheostomy was performed. Monopolar and bipolar cautery were used in independent experiments to assess the ignition capacity. Seven trials were performed for each fraction of inspired oxygen (FiO2): 1.0, 0.9, 0.7, 0.6, 0.5, 0.4, and 0.3. The primary outcome was ignition of a fire. The time was started once the cautery function was turned on. The time was stopped when a flame was produced. Thirty seconds was used as the cut‐off for “no fire.” Results The average time to ignition for monopolar cautery at FiO2 of 1.0, 0.9, 0.8, 0.7, and 0.6 was found to be 9.9, 6.6, 6.9, 9.6, and 8.4 s, respectively. FiO2 ≤ 0.5 did not produce a flame. No flame was created using the bipolar device. Dry tissue eschar shortened the time to ignition, whereas moisture in the tissue prolonged the time to ignition. However, these differences were not quantified. Conclusion Dry tissue eschar, monopolar cautery, and FiO2 ≥ 0.6 are more likely to result in airway fires.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".