Bibliographic record
Abstract
OBJECTIVES: : Overactive bladder (OAB) is a symptom complex that may be objectively assessed using urodynamic variables. The purpose of this study was to determine the correlation between OAB symptoms and low cystometric capacity (LCC). METHODS: : Between October and December 2005, 102 patients from the Urogynaecology Clinic at Mount Sinai Hospital participated in this prospectively planned, blinded, cross-sectional study. Participants underwent multichannel urodynamic testing and completed a symptom questionnaire focusing on urinary urgency, frequency, and urgency incontinence, in addition to recording fluid and caffeine intake. Answers were combined into symptom complexes and dichotomized into positive and negative responses. The diagnostic accuracy of OAB symptoms was assessed by calculating sensitivity, specificity, likelihood ratios, positive and negative predictive values, and Cohen's kappa, using urodynamic bladder capacity <350 mL as the reference. RESULTS: : Fifty-seven percent (58) of participants met our urodynamic diagnosis of LCC with a bladder capacity <350 mL. Of the patients, 49% (50) had a questionnaire result that was classified as positive for overactive bladder. The questionnaire had a specificity of 61%, a likelihood ratio of 1.47, a sensitivity of 57%, and a positive predictive value of 66%. With a Cohen's κ = 0.2, there was poor reliability. Reanalysis using an alternative method of dichotomizing, a bladder capacity cut-off of <300 mL, and the addition of information regarding fluid intake, did not improve accuracy measures. CONCLUSIONS: : Symptom history of OAB does not correlate with low cystometric capacity. We need further research to determine the accuracy of subjective and objective findings in patients with OAB, to provide the most accurate diagnosis in the least invasive manner.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".