Patients treated for uric acid stones reoccur more often and within a shorter interval in comparison to patients treated for calcium stones
Bibliographic record
Abstract
INTRODUCTION: We aimed to investigate the association between stone composition and recurrence rate in a well-characterized group of patients. METHODS: From our prospectively assembled database of 1328 patients undergoing ureteroscopy and percutaneous nephrolithotomy (PCNL) between 2010 and 2015, we identified 457 patients who met the inclusion criteria: a minimum of two years' followup, stone-free status following surgery, normal anatomy, and Fourier transform infrared (FT-IR) stone analysis results. Stone recurrence was identified by kidney-ureter-bladder (KUB) or an ultrasound (US). All symptomatic events were recorded. Kaplan-Meier and Cox proportional hazard regression methods were used to assess the differences in recurrence rates and associated risk factors. RESULTS: Calcium oxalate (CaOx), uric acid (UA), and struvite stones were found in 298 (65.2%), 99 (21.7%), and 28 (6.1%) patients, respectively. During a median followup of 38 months (interquartile range [IQR] 31-48), stone recurred in 111 (24%) patients. One-year stone-free rates (SFRs) stratified by composition were: CaOx 98%, UA 91.9%, calcium phosphate 90%, struvite 88%, and, cystine 83%; the two-year SFRs were 92.6%, 82.7%, 80%, 73%, and 75%, respectively. On multivariate Cox regression analysis, UA composition, the absence of medical preventive therapy, and preoperative stone burden were associated with a shorter time to recurrence. Secondary intervention for recurrent, symptomatic stones was required in 11 (11.1%) and 22 (7.4%) of patients with UA and CaOx stones, respectively (p=0.02). CONCLUSIONS: UA stone-formers are more likely to have a recurrence and to undergo surgical intervention in comparison to CaOx stone-formers, regardless of medical preventive treatment. These differences are more prominent during the first year of followup and should be incorporated into the patient's followup protocol.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".