Bibliographic record
Abstract
Tumour localization of small deep subpleural lesions during VATS depends on the differential tissue stiffness between a tumour and the adjacent tissue.To obtain a quantitative measure of stiffness, we catalogued the Young's Modulus of in situ abnormal lung lesions and adjacent tissue from resected specimens.A 5 to 10 mm section of resected tumour and adjacent lung tissue was placed in a custom indenting elastometer device to measure the Young's Modulus.The device applied a uniform force on the tissue samples to varying levels of tissue displacement -up to 15% of their thickness.The Young's Modulus was calculated from 200 measurements of forces and displacements.Each set of measurements was repeated three times.The Young's Modulus for each tissue histology was assessed by parametric or nonparametric analyses as appropriate.The median [range] Young's Modulus for all lung tumours (12.73 kPa [2.68-199.10];p < 0.001) was significantly higher than adjacent lung tissue (6.12 kPa [1.65-13.19];p < 0.001).Adenocarcinomas, squamous cell carcinomas, various metastases, and granuloma/fibromas had Young's Modulus values greater than adjacent lung histologies.This is the first study to report the elastic properties of human lung parenchyma in various disease states and abnormal lesions.There was a significant difference between the Young's Moduli of human lung tumours and parenchyma.These findings may aid in the development of improved intraoperative localization technologies for minimally invasive pulmonary surgeries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".