Development of a Symptom Score for Dysfunctional Elimination Syndrome
Bibliographic record
Abstract
PURPOSE: Dysfunctional elimination syndrome is a heterogeneous syndrome with no widely accepted diagnostic criteria. Previously developed questionnaires provide incomplete psychometric assessment. We developed a discriminative questionnaire for diagnosing dysfunctional elimination syndrome and assessed its validity and reliability. MATERIALS AND METHODS: A 14-item 5-point Likert scale questionnaire was devised using literature review, expert opinions and patient input. The questionnaire was administered to 62 children 4 to 16 years old (median age 8) clinically diagnosed with dysfunctional elimination syndrome by a pediatric urologist, of whom 71% were female. It was also administered to 50 healthy controls 4 to 16 years old (median age 7), of whom 66% were female. Children with structural abnormalities were excluded from study. To assess reliability 50 participants were asked to complete the questionnaire again 1 week later. RESULTS: Median total score in cases and controls was 14 of 52 (range 4 to 30) and 6 of 52 (range 1 to 13), respectively. The difference was statistically significant (p = 0.001). Discriminant function analysis showed 80% accuracy. ROC curve showed a score of 11 as the optimum threshold with an AUC of 0.903 (95% CI 0.814-0.948). Test-retest reliability was 84.5% (p = 0.001). Factor analysis showed unloading on 4 factors, corresponding to urinary incontinence, urgency, obstructive symptoms and constipation/fecal soiling. Of participants 85% classified the questionnaire as very easy or easy to complete. CONCLUSIONS: This new questionnaire is valid and reliable for diagnosing dysfunctional elimination syndrome. It can be used as a clinical or research instrument.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".