Air leaks: stapling affects porcine lungs biomechanics
Bibliographic record
Abstract
Abstract During thoracic operations, surgical staplers resect cancerous tumors and seal the spared lung. However, post-operative air leaks are undesirable clinical consequences: staple legs wound lung tissue. Subsequent to this trauma, air leaks from lung tissue into the pleural space. This affects the lung’s physiology and patients’ recovery. The objective is to biomechanically and visually characterize porcine lung tissue with and without staples in order to gain knowledge on air leakage following pulmonary resection. Therefore, a syringe pump filled with air inflates and deflates eleven porcine lungs cyclically without exceeding 10 cmH 2 O of pressure. Cameras capture stereo-images of the deformed lung surface at regular intervals while a microcontroller simultaneously records the alveolar pressure and the volume of air pumped. The raw images are then used to compute tri-dimensional displacements and strains with the Digital Image Correlation method (DIC). Air bubbles originated at staple holes of inner row from exposed porcine lung tissue due to torn pleural on costal surface. Compared during inflation, left upper or lower lobe resections have similar compliance (slope of the pressure vs volume curve), which are 9% lower than healthy lung compliance. However, lower lobes statistically burst at lower pressures than upper lobes ( p- value<0.046) in ex vivo conditions confirming previous clinical in vivo studies. In parallel, the lung deformed mostly in the vicinity of staple holes and presented maximum shear strain near the observed leak location. To conclude, a novel technique DIC provided concrete evidence of the post-operative air leaks biomechanics. Further studies could investigate causal relationships between the mechanical parameters and the development of an air leak.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".