Does 24-Hour Urine Supersaturation Predict Stone Composition?
Bibliographic record
Abstract
Background: The aim of the study was to evaluate the correlation between 24-hour urine supersaturation (SS) levels and the crystalline stone composition. Methods: We retrospectively reviewed the results of stone analysis of 386 patients who had completed 24-hour urine stone risk profiles within 2 months of stone analysis. Patients were characterized as calcium oxalate (CAOX), calcium phosphate (CAPH) or uric acid (UA) stone formers based on the predominant component (> 60%) of their stone. Patients with < 50% of one stone composition were characterized as a mixed stone former. Sensitivity, specificity and accuracy of the 24-hour urine SS for predicting the corresponding stone component were calculated. Results: The distribution of stone compositions was 235 (61%) CAOX, 98 (25%) CAPH, 35 (9%) UA and 18 (5%) mixed stone group. At predominant stone mineral concentration >= 60%, the accuracy of 24-hour urine SS for predicting the predominant stone composition was 52.5% for CAOX, 70% for CAPH and 67% for UA group. Even when the predominant stone mineral concentration was >= 90%, the accuracy of SS did not improve: COAX (49%, P = 0.6641), CAPH (77%, P = 0.361) and UA (67%, P = 0.9593). Conclusions: Twenty-four-hour urine SS has a poor accuracy to predict the predominant stone composition. Accuracy is highest for patients with CAPH stones. World J Nephrol Urol. 2015;4(1):169-172 doi: http://dx.doi.org/10.14740/wjnu206w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".