The Use of Standardized Questions in Identifying Patients with Dyssynergic Defecation
Bibliographic record
Abstract
BACKGROUND: Dyssynergic defecation (DD) is present in approximately 30% of patients with idiopathic chronic constipation (CC). Diagnostic criteria for DD require objective testing such as anorectal manometry (ARM); yet, ARM remains a limited resource in Canada. The aim of this study is to determine the predictability of DD in patients with CC using a standardized self-reported symptom questionnaire. METHOD: In this study, 166 consecutive English-speaking patients with CC who were referred for ARM completed a symptom questionnaire. DD was diagnosed if pelvic floor dyssynergy was demonstrated by ARM and balloon expulsion time was more than one minute. Likelihood ratios (LRs) were calculated for individual symptoms and prespecified symptom combinations. Likelihood ratios greater than five or less than 0.2 were considered significant. A recursive partitioning tree was used to find the symptoms best able to predict DD. RESULTS: No single constipation symptom was sufficient to predict a diagnosis of DD. Patients who reported sometimes feeling an urge to defecate and a prolonged straining duration of greater than five minutes were more likely to have DD (LR = 7.74). In patients who reported straining often or always and had a short straining duration of less than two minutes, the diagnosis of DD was less likely (LR = 0.04). The recursive partitioning tree analysis similarly identified a sense of urge with a prolonged straining duration as predictor for DD, as well as an incomplete evacuation as another potential predictor. CONCLUSION: Questions regarding need to strain, duration of straining, urge to defecate, and incomplete evacuation are useful to predict the presence of DD in patients with CC. These questions will enable clinicians to make a clinical diagnosis of DD to guide treatment.
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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 | 0.006 | 0.023 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".