Minimally invasive removal of obstructive ureteral stones by intracorporeal lithotripsy in horses: 3 patients.
Bibliographic record
Abstract
Three client-owned horses diagnosed with obstructive ureteral stones were referred and treated in a minimally invasive manner by retrograde ureteroscopy in conjunction with electrohydraulic lithotripsy (EHL) or laser Holmium:YAG lithotripsy (HYL). For all 3 horses, additional tests revealed variable degrees of azotemia and ureteral obstruction. Ultrasound examination (2 horses) revealed a loss of cortico-medullary distinction consistent with a chronic nephropathy. Ultrasound-guided biopsy of the right kidney in 1 horse revealed moderate glomerulosclerosis and lymphoplasmacytic nephritis. A standing anesthesia with a coccygeal epidural was done for each horse. A perineal urethrotomy was performed in 2 horses before the urethrocystoscopy. One horse was treated with Holmium:YAG laser lithotripsy and 2 others were treated using a electrohydraulic lithotripsy probe. Each procedure was successful. The ureteroscopy was successfully performed and visualization was excellent. Fragmentation of stones seemed easier with the electrohydraulic lithotripsy probe. No complications, pain, or signs of discomfort after the procedure were noticed. All 3 horses were discharged from the hospital. Key clinical message: Obstructive ureteral stones in horses can be successfully treated in a minimally invasive manner by retrograde ureteroscopy accompanied by lithotripsy. This technique is safe, not painful and did not require general anesthesia. Electrohydraulic lithotripsy appeared superior for stone fragmentation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".