© 2002 Canadian Medical Association or its licensors
Bibliographic record
Abstract
Kidney stones are rarely, if ever, fatal. The main im-pact of nephrolithiasis is felt by young, otherwisehealthy adults in the form of acute renal colic, causing symptoms of pain, nausea, vomiting and hematuria. The estimated cost of this condition in the United States for 1993 was US$1.83 billion.1 The lifetime risk of passing a kidney stone is about 8%–10 % among North American males, and the peak age of incidence is 30 years. The rate of kidney stone formation in women is about half that in men, with 2 peaks, the first among women aged 35 years and the second among those aged 55 years.2 Among pa-tients who have passed one kidney stone, the lifetime recur-rence rate is 60%–80%.2 There is significant geographic and seasonal variation in rates of stone formation. The rea-sons for this variation are not entirely clear, but they may relate to climate and the mineral content of drinking water. The frequency of kidney stones (notably calcium oxalate stones) has increased with improved standards of living.2 This review will not concentrate on the acute manage-ment of nephrolithiasis but deals with the investigation and management of recurrent nephrolithiasis. Pathophysiology Kidney stone formation is the end result of a physico-chemical process that involves nucleation of crystals from a supersaturated solution. The common constituents of kid-ney stones are listed in Table 1. The factors that influence crystal generation are urine volume, concentration of stone constituents (a function of urine volume), the presence of a nidus and the balance among various physicochemical fac-tors that inhibit or promote stone formation. Most people’s urine is supersaturated with the common components of renal stones, including calcium phosphate, calcium oxalate and, frequently, uric acid. Supersaturation of the urine constitutes a driving force within the solution favouring crystal nucleation and growth.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.851 | 0.810 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".