Optimizing the Utility of Anorectal Manometry for Diagnosis and Therapy: A Roundtable Review and Recommendations
Bibliographic record
Abstract
BACKGROUND & AIMS: Anorectal manometry (ARM) is a comprehensive diagnostic tool for evaluating patients with constipation, fecal incontinence, or anorectal pain; however, it is not widely utilized for reasons that remain unclear. The aim of this roundtable discussion was to critically examine the current clinical practices of ARM and biofeedback therapy by physicians and surgeons in both academic and community settings. METHODS: Leaders in medical and surgical gastroenterology and physical therapy with interest in anorectal disorders were surveyed regarding practice patterns and utilization of these technologies. Subsequently, a roundtable was held to discuss survey results, explore current diagnostic and therapeutic challenges with these technologies, review the literature, and generate consensus-based recommendations. RESULTS: ARM identifies key pathophysiological abnormalities such as dyssynergic defecation, anal sphincter weakness, or rectal sensory dysfunction, and is a critical component of biofeedback therapy, an evidence-based treatment for patients with dyssynergic defecation and fecal incontinence. Additionally, ARM has the potential to enhance health-related quality of life and reduce healthcare costs. However, it has significant barriers that include a lack of education and training of healthcare providers regarding the utility and availability of ARM and biofeedback procedures, as well as challenges with condition-specific testing protocols and interpretation. Additional barriers include understanding when to perform, where to refer, and how to use these technologies, and confusion over billing practices. CONCLUSIONS: Overcoming these challenges with appropriate education, training, collaborative research, and evidence-based guidelines for ARM testing and biofeedback therapy could significantly enhance patient care of anorectal disorders.
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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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".