PREDICTION OF VESICOURETHERAL REFLUX IN URINARY TRACT INFECTION: TOWARDS A CLINICAL DECISION RULE
Bibliographic record
Abstract
During the past decade, many paediatric societies have recommended that all young children undergo a cystography after a first febrile urinary tract infection (UTI). This systematic strategy offered a 100% sensitivity, but without any specificity, meaning that many children underwent an unnecessary cystography, a painful, irradiating and expensive examination. Regarding the recent publications minimising the clinical consequences of low-grade vesicoureteral reflux (VUR), the low rate of high-grade VUR and the current discussions on high-grade VUR treatment, new guidelines propose never to perform a cystography with the risk of recurring UTI and renal scarring. Between both these ends, an evidence-based strategy would find a place to predict high-grade VUR to avoid unnecessary cystography because cystographies miss the least number of patients with high-grade VUR. Procalcitonin has been found and validated to be a strong and sensitive predictor of VUR and especially high-grade VUR in single-centre then multicentre studies (Pediatrics 2005; J Pediatr 2007). The high sensitivity of procalcitonin was then confirmed in children with a DMSA scan-confirmed acute pyelonephritis. However, prediction tools should take into account that clinicians should probably not be ready to make their decision only on a single newly identified biological marker. We recently found performing a systematic review and meta-analysis that ureteral dilation seemed the best renal ultrasound criterion to predict both all-grade and high-grade VUR with a high specificity but a low sensitivity. Therefore, combining such a renal ultrasound criterion with procalcitonin in a clinical decision rule predicting high-grade VUR could be clinically relevant and will be presented.
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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 | 0.036 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".