Lung thermography during the initial reperfusion period to assess pulmonary function in cellular ex vivo lung perfusion
Bibliographic record
Abstract
BACKGROUND: Thermography is a noninvasive technology to detect low temperatures in poorly circulated areas. In ex vivo lung perfusion (EVLP), lungs are rewarmed to body temperature during the initial 1 h. Currently, the effect of graft thermal changes during the rewarming phase on pulmonary function is unknown. In this study, we evaluated the correlation of lung surface temperature with physiological parameters, wet/dry ratio, and transplant suitability in Lund-type EVLP. METHODS: Fifteen pigs were divided into three groups: control group (no warm ischemia) or donation after circulatory death groups with 60 or 90 min of warm ischemia (n = 5, each). Thermal images of the lower lobes were continuously collected from the bottom of an organ chamber using infrared thermography throughout EVLP. RESULTS: At 8 min, lung surface temperatures of nonsuitable cases were significantly lower than in suitable cases (25.1 ± 0.6 vs. 27.8 ± 1.2°C, p < 0.001), while there was no difference in lung surface temperatures between the two groups at 0-4 min and 12-120 min. There was a significant negative correlation between lung surface temperatures at 8 min and wet/dry ratio at 2 h in the lower lobes (R = -0.769, p < 0.001, cutoff = 26°C, area under the curve = 1.0). A lung surface temperature of <26°C was significantly correlated with poor pulmonary function and transplant nonsuitability. CONCLUSION: A lung surface temperature of ≥26°C at 8 min is a good early predictor of transplant suitability in cellular EVLP and might be applicable in clinical EVLP.
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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 | 0.000 |
| 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 | 0.000 |
| 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".