Bibliographic record
Abstract
Introduction: Kidney stones disease is a medical condition when hard deposites made of various minerals and substances form inside kidneys. It may lead to clinical symptoms as severe pain felt in the belly area or side of the back. Also it can radiate and reach testicles, groin area, labia. Other symptoms may include blood in the urine, abnormal color of urine, fever, nausea, vomiting. Because of growing incidence rate- adequate question is how can we protect ourselves from kidney stones development by changing habits. Aim of study: Fundamental aim of study is to demonstrate how daily, routine habits changes may have a huge impact on kidney stones development risk. State of knowledge: There are a lot of confirmed parameters and factors which can lead to kidney stones devolopment. Factors that increase risk of developing kidney stones include: dehydration, diet (meat intake, fruit/vegetables intake, sodium intake), family or personal history, obesity, digestive diseases and surgery, metabolic disorders, urinary track infections, anatomical abnormalities- obstruction of the kidney, calyceal diverticulum, horseshoe kidney, ureterocele. This group includes many modified factors, so lifetime risk of kidney stones depends also on our habits and way of life. Conclusion: There are many modified factors which may lead to this disease and because of that it is crucial to improve patients and whole society awareness about them. By appropriate daily habits changes we can significantly reduce risk of lifetime kidney stone disease development. It is important to know that concrete reports and analyses were made and they demonstrated scientifically proven correlations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".