Use of the AccuVein AV400 during RARP: an infrared augmented reality device to help reduce abdominal wall hematoma.
Bibliographic record
Abstract
INTRODUCTION: Abdominal wall hematoma (AWH) is a self-resolving, yet common complication from the insertion of trocars during laparoscopic surgery. Particularly, its appearance may increase patient anxiety and may reduce overall surgical satisfaction. MATERIALS AND METHODS: In a retrospective study analyzing data from 724 robot-assisted radical prostatectomy cases (RARP), trocar insertion sites were examined on postoperative day 7 with Foley removal for AWH. AWH was defined by a sizable collection of blood below the skin as a result of the surgery. The AccuVein AV400 system was utilized to generate real-time images of venous structures beneath the skin. Comparative outcomes were performed with a series of 114 men where the AccuVein AV400 device was applied over trocar insertion markings to help modify port placement. RESULTS: The pre-incision imaging of the AccuVein system modified port placement in 74 of 114 cases (65%), and reduced AWH from 8.8% to 2.6% (p = 0.03) as compared to transabdominal illumination. Port placement adjustments were most prevalent in the lateral regions of the abdomen, prompting attention for lateral trocar insertion to avoid vessels such as the thoracoepigastric veins. Notably, the body mass index (BMI) of patients experiencing AWH who received the pre-incision imaging of AccuVein was significantly higher than patients receiving standard transabdominal illumination (34.2 and 27.9 kg/m² respectively; p = 0.02). CONCLUSION: The AccuVein AV400 device appears to be an effective adjuvant for decreasing rates of AWH during lower abdominal wall trocar insertion, though its effectiveness is limited in patients with extreme BMI. Additionally, special attention should be directed towards trocar insertion in the lateral regions of the abdomen.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".