Renal matrix stone managed by ureteroscopic holmium laser lithotripsy.
Bibliographic record
Abstract
INTRODUCTION: Matrix stones are rare types of urinary calculi composed of mucoproteins and mucopolysaccharides. Since isolated flank pain may be the only presenting symptom and routine radiographic studies are usually non-informative, diagnosis of such urinary calculi represents a clinical challenge. Traditionally, these matrix stones have been managed by either open pyelolithotomy or percutaneous nephrolithotomy (PCNL). Ureteroscopic management of a patient with matrix renal stones and review of literature is presented. CASE REPORT: A 34-year-old woman presented with chronic right flank pain. Abdominal ultrasound found a 5.3 cm heterogeneous right renal pelvic mass with 9.7 mm stone. CT urogram confirmed the filling defects. Diagnosis of matrix stones was made using ureteroscopy. During ureteroscopy and holmium laser lithotripsy, a 13/15F ureteral access sheath was placed and the matrix stones were irrigated out. She required outpatient shockwave lithotripsy for the residual radio-opaque stone. A second-look ureteroscopy confirmed stone free status. COMMENT: Matrix renal stones present a diagnostic challenge. Although PCNL is the gold standard of therapy for large renal matrix stones, ureteroscopy could also be used for both diagnosis and laser lithotripsy. In the present case, ureteral access sheath was used to irrigate the mucinous matrix stone material.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| 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".