Expert Consensus on Pediatric Urodynamics Reporting Using Modified Delphi Technique
Bibliographic record
Abstract
PURPOSE: Urodynamic testing (UDS) is an important tool in the management of pediatric lower urinary tract conditions. There have been notable efforts to standardize pediatric UDS nomenclature and technique, but no formal guidelines exist on essential elements to include in a clinical report. We sought to identify ideal structure and elements of a pediatric UDS assessment based on expert consensus. MATERIALS AND METHODS: Pediatric urologists regularly performing UDS were queried using a Delphi process. Participants were invited representing varied geographic, experience, and societal involvement. Participants underwent 3 rounds of questionnaires between November 2022 and August 2023 focusing on report organization, elements, definitions, and automated electronic health record clinical decision support. Professional billing requirements were also considered. Consensus was defined as 80% agreeing either in favor of or against a topic. Elements without consensus were discussed in subsequent rounds. RESULTS: A diverse sample of 30 providers, representing 27 institutions across 21 US states; Washington, District of Columbia; and Canada completed the study. Participants reported interpreting an average number of 5 UDS reports per week (range 1-22). The finalized consensus report identifies 93 elements that should be included in a pediatric UDS report based on applicable study conditions and findings. CONCLUSIONS: This consensus report details the key elements and structure agreed upon by an expert panel of pediatric urologists. Further standardization of documentation should aid collaboration and research for patients undergoing UDS. Based on this information, development of a standardized UDS report template using electronic health record implementation principles is underway, which will be openly available for pediatric urologists.
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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 | 0.347 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".