Application of novel burst wave lithotripsy and ultrasonic propulsion technology for the treatment of ureteral calculi in a bottlenose dolphin (Tursiops truncatus) and renal calculi in a harbor seal (Phoca vitulina)
Bibliographic record
Abstract
Marine mammals may develop kidney stones, which can be challenging to treat. We describe burst wave lithotripsy (BWL) and ultrasonic propulsion to treat ureteral calculi in a 48-year-old female bottlenose dolphin (Tursiops truncatus) and to reduce renal stone burden in a 23-year-old male harbor seal (Phoca vitulina). BWL and ultrasonic propulsion were delivered transcutaneously in sinusoidal ultrasound bursts to fragment and reposition stones. Targeting and monitoring were performed with real-time imaging integrated within the BWL system. Four dolphin stones were obtained and fragmented ex vivo. The dolphin case received a 10-min and a 20-min BWL treatment conducted approximately 24 h apart to treat two 8-10 mm partially obstructing right mid-ureteral stones, using oral sedation alone. For the harbor seal, while under general anesthesia, retrograde ureteroscopy attempts were unsuccessful because of ureteral tortuosity, and a 30-min BWL treatment was targeted on one 10-mm right kidney stone cluster. All 4 stones fragmented completely to < 2-mm fragments in < 20 min ex vivo. In the dolphin case, the ureteral stones appeared to fragment, spread apart, and move with ultrasonic propulsion. On post-treatment day 1, the ureteral calculi fragments shifted caudally reaching the ureteral orifice on day 9. On day 10, the calculi fragments passed, and the hydroureter resolved. In the harbor seal, the stone cluster was observed to fragment and was not visible on the post-operative computed tomography scan. The seal had gross hematuria and a day of behavior indicating stone passage but overall, an uneventful recovery. BWL and ultrasonic propulsion successfully relieved ureteral stone obstruction in a geriatric dolphin and reduced renal stone burden in a geriatric harbor seal.
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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 | 0.000 |
| 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) | 0.000 | 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".