Intraoperative ultrasound examination is useful for monitoring transplanted islets
Bibliographic record
Abstract
A 39-y-old man, who had an episode of pancreatic bleeding due to chronic pancreatitis, received total pancreatectomy with islet autotransplantation (TP with IAT). Intraoperative ultrasound (US) examination was done to detect transplanted islets and evaluate the quality of US imaging. Islet isolation from the resected total pancreas was performed and approximately 230,000 islet equivalents (IEQ) (the tissue volume was 600 µL and the purity was 30%) were acquired. A double lumen catheter, used for transplantation and for monitoring the portal vein pressure, was inserted into the portal vein via the superior mesenteric vein, and the tip of the catheter was positioned at the bifurcation of the anterior and posterior branch of the portal vein to selectively infuse the islets into the right lobe of the liver in order to prevent total liver embolization. Intraoperative US examination (central frequency 7.5 MHz, Nemio™ XG, Toshiba Medical System Co.) was started at the same time as the transplantation. US examination revealed the transplanted islets as hyperechoic clusters that flowed from the tip of the catheter to the periphery of the portal vein. There were no findings of portal thrombosis or bleeding in the US image, and also no increase of the portal vein pressure during transplantation. In conclusion, we succeeded in visualizing human islets using US, which enabled us to perform islet transplantation safely. The hyperechoic images were considered to be viable islets. Intraoperative US examination can be useful for detecting islets at transplantation in a clinical setting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".