Sugar and Stones
Bibliographic record
Abstract
Introduction Urolithiasis is a common urological problem in the United Kingdom. 6% of the adult population were diag-nosed with diabetes in England in 2013. Researchers suggest the association of diabetes with stone forma-tion, recurrence, and morbidity. This study aimed to compare the prevalence of risk factors like metabolic syndrome, urinary tract infections, age, gender and ethnicity among diabetics versus non-diabetics and to determine how diabetes affects the biochemical and surgical outcomes of urolithiasis. Methods There were182 patients treated surgically for urolithiasis between January 2010 and December 2012 were retrospectively analyzed. Information was cross-referenced with electronic notes to produce biochemical and surgical data. Results A total of 31 (17%) patients had type 2 diabetes. The mean age of diabetics was significantly higher than non-diabetics by nearly 12 years (p-value < 0.001). Hypertension, hyperlipidaemia, obesity and UTIs were more prevalent among diabetics (p-value < 0.001, < 0.001, 0.01, 0.009 respectively). Diabetics had signifi-cantly bigger mean stones size (p-value=0.008) and are at higher risk of stone recurrence at 1 year (p-value =0.04) than non-diabetics. Stone recurrence was not significant at 3 and 5 years between the two groups. Diabetics significantly had higher urinary oxalate, and nearly statistically significant lower phosphate levels (p-value=0.007, 0.076 respectively). Conclusions Diabetics were significantly older and associated with metabolic syndrome. UTIs were more prevalent among diabetics which put them at risk of postoperative complications. Diabetics are at higher risk of stone recurrence at 1 year compare to non-diabetics. Biochemical urinary findings are important as they can guide the management of recurrent stone formers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".