Comparison of Two Histological Test Methods for the Measurement of Thermal Spread Associated With Energized Vessel Sealing
Bibliographic record
Abstract
Abstract Development of safe and effective radio frequency (RF) vessel sealing devices requires the complete characterization of thermal damage that can occur adjacent to areas of energy application. Hematoxylin and eosin (H&E) staining is an efficacious and extensively used histological test method for assessing this lateral thermal damage through characterization of changes in cell and tissue morphology. The purpose of this investigation was to compare thermal spread measurements obtained using H&E staining to measurements made using picrosirius red (PSR) staining, an alternative histological test method that exploits the birefringence of healthy vascular collagen to facilitate the identification of thermal damage associated with use of vessel sealing devices. A 5 mm RF vessel sealing device was used to seal arteries in vivo in various porcine tissues. Subsequently, sealed vessel segments were excised and histologically processed. Paired histological sections were mounted on slides; one section was stained with PSR and one section was stained with H&E. A blinded board-certified veterinary pathologist completed thermal spread measurements on all PSR- and H&E-stained sections. Bland–Altman comparison plots were then used to estimate any bias between the two measurement methods, as well as quantify the range of differences between PSR and H&E thermal spread measurements. The magnitude of the mean difference between measurements was determined to be less than 0.10 mm and, with 90 % confidence, 95 % of the differences between measurements were within 1.0 mm. These results indicate that PSR-staining is an appropriate alternative to H&E-staining for the measurement of lateral thermal spread associated with an RF vessel sealing device.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".