MP76-12 DETERMINING PARENTAL PREFERENCES FOR TREATMENT OF VESICOURETERAL REFLUX: PROFILE CASE BEST-WORST SCALING
Bibliographic record
Abstract
You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Value of Care: Cost & Outcomes Measures I1 Apr 2018MP76-12 DETERMINING PARENTAL PREFERENCES FOR TREATMENT OF VESICOURETERAL REFLUX: PROFILE CASE BEST-WORST SCALING Zachary Dionise, Michael Garcia-Roig, Andrew Kirsch, and Jonathan Routh Zachary DioniseZachary Dionise More articles by this author , Michael Garcia-RoigMichael Garcia-Roig More articles by this author , Andrew KirschAndrew Kirsch More articles by this author , and Jonathan RouthJonathan Routh More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.2580AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Vesicoureteral reflux (VUR) is a common pediatric condition with several potential treatment options, including antibiotic prophylaxis, endoscopic injection, minimally-invasive, or open surgery. Parents must weigh attributes such as effectiveness, cost, and complication rates to make value judgments about the “best” treatment. The objective was to define parental preferences for VUR treatments using profile case best-worst scaling (BWS), a novel technique. METHODS Preference data from a community sample of US parents were collected via survey instrument with multimedia VUR introduction published on Amazon's Mechanical Turk online work interface. The survey used a profile case BWS experiment to evaluate relative desirability of VUR attribute levels. Attributes and their levels were selected based on extensive review of the VUR literature (Table). A balanced, orthogonal BWS design was constructed and data were analyzed using 4 complimentary analysis techniques: best minus worst (frequency a level was chosen best minus frequency it was chosen worst, B-W), multinomial logistic regression (MNL) constructed probability scaled values (scaled values that describe the likelihood a given level is chosen in the survey, LPS), and latent class analysis (LCA) all using Sawtooth Software Lighthouse Studio 9.5.2. RESULTS 248 parents completed the instrument. By B-W and MNL, 97% effectiveness of treatment cure is the most desirable attribute level compared to all other attribute levels (B-W 833, all p<.01), while 53% effectiveness is least desirable (B-W -514, all p<.01). Complication rate <1% and 88% effectiveness are each more desirable than a doctor's recommendation of a given treatment (all p<.05). Further, 88% and 97% effectiveness, doctor recommendation, and complication rate <1% are each more desirable than an outpatient or a lowest societal cost treatment (all p<.02). Two-group LCA revealed a subgroup for which doctor recommendation and minimal hospitalization are particularly desirable, while low effectiveness is especially undesirable (Table, Class A). CONCLUSIONS When evaluating VUR treatment alternatives, high level of clinical cure is by far the most desirable attribute. Along with effectiveness, low complication rate is more desirable than a doctor's treatment recommendation. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e1023 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Zachary Dionise More articles by this author Michael Garcia-Roig More articles by this author Andrew Kirsch More articles by this author Jonathan Routh More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".