Do Age and Sex Influence Anorectal Manometry Parameters?
Bibliographic record
Abstract
Abstract Background High-resolution anorectal manometry (HRM) is widely used in the evaluation of anal incontinence and constipation, which become increasingly prevalent with age. However, the impact of age and comorbidities on physiological digestive parameters remains poorly understood. In this study, we aimed to evaluate the effect of age on anorectal function. Methods We conducted a retrospective study on patients at our digestive motility clinic between January 2016 and May 2019. All patients with a normal HRM were included. Clinical data and HRM parameters were collected in a database. Multivariate regression analyses were performed to evaluate the effects of age, sex, medical comorbidities and obstetric history on anorectal HRM parameters. Key Results One hundred and forty-four patients were included (mean age: 53 ± 16 years, 72% females). The main indications for anorectal HRM were incontinence (44%), constipation (37%) and anorectal pain (9%). Age was significantly associated with higher maximum tolerable volume (β = +0.48 mL year-1, P = 0.04) and higher rectal compliance (β = +0.04 mL year-1, P = 0.01). Independently from age and medical comorbidities, female demonstrated significantly lower mean endurance squeeze pressure (β = −44.4 mmHg, P < 0.001), maximal squeeze pressure (β = −62.3 mmHg; P < 0.001), volume at first urge (β = −16.7 mL, P = 0.02) and maximum tolerable volume (β = −16.1 mL, P = 0.046). Vaginal birth was associated with lower tolerable maximum pressure (β = −39.4 mmHg, P = 0.046). Conclusion Age and sex are independent factors which influence anorectal HRM parameters. These findings should be taken into consideration when interpreting anorectal HRM.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".