Hydro-Jet–Assisted Pneumonectomy: A New Technique in a Porcine Model
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hydro-jet technology has long been used to cut various materials, such as metal and wood, in the industrial field. In the medical field, this technology has been applied successfully in selective cutting of the parenchyma of the liver. However, to our knowledge, no data are available on the use of the hydro-jet technique for pneumonectomy. The purpose of this study was to evaluate a new dissection technique in which a high-pressure water stream (hydro-jet) and a new dissection probe for pulmonary resection are used. METHODS: Thirty pigs underwent right pneumonectomy. Pigs were randomized to either the conventional or hydro-jet-assisted dissection technique. The feasibility of this technique and the features of surgical dissection were evaluated and compared between the two groups. RESULTS: Pneumonectomy was successful in all animals. The mean operative times were 55 and 65 minutes and the mean volumes of blood loss were 37 and 65 mL for the hydro-jet and conventional dissection techniques, respectively. Complications included vascular injury in 6% and 20% of cases with the hydro-jet and conventional techniques, respectively. The use of hydro-jet for pneumonectomy had clear technical advantages over the conventional dissection. Hydro-jet resulted in a selective dissection of fibrous and connective tissue, preserving blood vessels for later ligation. Therefore, the dissection was performed in a relatively bloodless field. The ease of dissection with the bent-tip dissector represents another advantage. The continuous water flow allows a clear view for the operator. CONCLUSIONS: This study shows that hydro-jet dissection represents an excellent alternative to the conventional technique for pulmonary resection. The improved anatomic dissection combined with an almost bloodless operating field secondary to continuous water flow may decrease dissection-related complications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".