3D thoracoscopic ultrasound volume measurement validation in an<i>ex vivo</i>and<i>in vivo</i>porcine model of lung tumours
Bibliographic record
Abstract
The purpose of this study was to validate the accuracy and reliability of volume measurements obtained using three-dimensional (3D) thoracoscopic ultrasound (US) imaging. Artificial "tumours" were created by injecting a liquid agar mixture into spherical moulds of known volume. Once solidified, the "tumours" were implanted into the lung tissue in both a porcine lung sample ex vivo and a surgical porcine model in vivo. 3D US images were created by mechanically rotating the thoracoscopic ultrasound probe about its long axis while the transducer was maintained in close contact with the tissue. Volume measurements were made by one observer using the ultrasound images and a manual-radial segmentation technique and these were compared with the known volumes of the agar. In vitro measurements had average accuracy and precision of 4.76% and 1.77%, respectively; in vivo measurements had average accuracy and precision of 8.18% and 1.75%, respectively. The 3D thoracoscopic ultrasound can be used to accurately and reproducibly measure "tumour" volumes both in vivo and ex vivo.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".