Patient and Stone Characteristics Associated with Surgical Intervention in Pediatrics
Bibliographic record
Abstract
BACKGROUND: The incidence of kidney stones in children is increasing. While guidelines exist for acute surgical intervention, there is limited data to inform the decision as to when to intervene non-urgently. OBJECTIVES: To identify patient and stone characteristics predicting stone surgery in children. DESIGN: Retrospective chart review. SETTING: Stollery Children's Hospital, Edmonton, Alberta, Canada from 1990 to 2013. PATIENTS: Sixty-three children aged 0-18 years old who presented with a total of 142 stones. MEASUREMENTS: Patient's surgical history, demographics, metabolic measures, and stone number, type, and location. METHODS: Univariate and multivariate analysis, controlling for presentation number and individual-level variation by repeated measures analysis were conducted to assess for patient and stone characteristics associated with surgical intervention. RESULTS: Sixty-five percent (41/63) required surgery during a mean follow-up of 19 months. Stone characteristics associated with surgical intervention by multivariate analysis included larger stone size (>6 mm), and stone composition of calcium oxalate. LIMITATIONS: Single center study with a limited sample size and duration of follow up, thereby limiting predictive power. There were some missing data (i,e. stone type was not always available). Despite this, stone type remained significant in multivariate modeling. CONCLUSION: Stone size > 6mm and composition with calcium oxalate but not patient age or symptoms associated with presentation predicted surgical intervention. These observations can be used to inform decisions as to whether urolithiasis should be surgically managed electively or observed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".