Bibliographic record
Abstract
INTRODUCTION: Understanding the composition of a kidney stone is crucial in leading to proper treatment and preventing reoccurring urolithiasis. This study aimed to investigate the prevalence of urolithiasis in the province of New Brunswick (NB), Canada. METHODS: A total of 3828 kidney stone analysis reports from October 1, 2016 to September 30, 2019, were reviewed from laboratory information systems. Among them, 3311 were identified as new cases. Stone compositions were analyzed by the Fourier transform infrared spectrometry. Incident rates were compared using Chi-squared analysis of different age, sex, and regional health authority (RHA) zones. RESULTS: =254, p<0.001) incident rate of 189 (95% confidence interval [CI] 182-198) than females (107 [95% CI 102-114]) per 100 000 person-years. Zone 1 had significantly higher (245 per 100 000 person-years, p<0.001) prevalence compared to other RHA zones. Age group over 65 years had the highest incidence rate of 253 per 100 00 person-years of all groups. The predominant kidney stone types in NB were calcium oxalate monohydrate (60.68%) and calcium oxalate dihydrate (11.58%). Those patients aged 0-18 years had a high percentage of struvite (4.32%) vs. the provincial average (2.19%) (p<0.001). CONCLUSIONS: The prevalence of NB's urolithiasis is slightly higher than that of Ontario. Since both zones 1.1 and 1.2 have significantly high prevalence and are situated in the Moncton area (combined zone 1), it may suggest that geographical factors play a role in the prevalence of urolithiasis in NB.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".