Bibliographic record
Abstract
Introduction: As little data exists on the nature and causes of pain in nonobstructing renal stones, our objective was to assess how disease-specific factors, global patient characteristics, and personality traits influence perceived symptoms.Methods: After consent was obtained, patients completed a standardized history, physical exam, and questionnaire sets.Enrollment was 2:1 asymptomatic patients (AP) to symptomatic patients (SP) with computed tomography-confirmed calyceal stones ≤5 mm, without focal signs of obstruction.Descriptive statistics and Student's t-tests were used to characterize and compare groups.Results: Our primary analysis included 28 patients (8 SP, 20 AP).There were no significant differences in age, gender, household income, or prevalence of functional pain syndromes (i.e., 25% vs. 27% IBS, FM, IC, etc.).All (100%) SP had prior stone events (vs.55% of AP).More AP endorsed chronic neck or back pain (25% vs. 12.5%), whereas SP reported worsened pain with physical activity (50% vs. 30%) and used more daily pain medication (62.5% vs 25%).Standardized assessment tools for pain and psychometric contributors showed SP have significantly higher Body Pain Index (12.2 vs. 43.3,p=0.005), and Pain Disability Index scores (5.9 vs. 23.8,p=0.004).SP also scored higher on catastrophizing (15.2 vs. 31.7,p=0.008), and kinesiophobia inventories (29.5 vs. 40.3,p=0.014).No significant differences were noted in the Modified Somatic Perception Questionnaire, or Hospital Anxiety and Depression Scale.The Wisconsin Stone QoL tool did not differ between groups, however, the more generalized 15D QoL tool showed a decreased overall health-related quality of life in SP (20.3 vs. 26.8,p=0.05).Conclusions: Our preliminary analysis of the SNORC cohort identifies potential psychometric contributors to symptomatic complaints related to stone disease.Future studies based on these findings will attempt to further define this challenging population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.243 | 0.091 |
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".