Low Anterior Resection Syndrome in a Reference North American Sample: Prevalence and Associated Factors
Bibliographic record
Abstract
BACKGROUND: Low anterior resection syndrome (LARS) is a well-described consequence of rectal cancer treatment. Studying the degree to which bowel dysfunction exists in the general population may help to better interpret to what extent LARS is related to disease and/or cancer treatment. Currently, North American LARS normative data are lacking. The aim of this study was to describe the prevalence of bowel dysfunction, as measured by the LARS score, and quality of life (QoL) in a reference North American sample. Quality of life was measured and associations between participant characteristics and LARS were identified. STUDY DESIGN: This was a single-institution cross-sectional study of asymptomatic adults who underwent screening and surveillance colonoscopies from 2018 to 2021 with no/benign endoscopic findings. Survey was conducted on select comorbidities, sociodemographic factors, LARS, and QoL. Outcomes were LARS and QoL. Multivariable linear regression accounting for a priori clinical factors associated with bowel dysfunction was performed. RESULTS: Of 1,004 subjects approached, 502 (50.0%) participated, and 135 (26.9%) participants had major/minor LARS. On multiple linear regression, female sex (β = 2.15, 95% CI 0.30 to 4.00), younger age (β = -0.10, 95% CI -0.18 to -0.03), White ethnicity (β = 2.45, 95% CI 0.15 to 4.74), and the presence of at least one of the following factors: diabetes, depression, neurologic disorder, or cholecystectomy (β = 3.54, 95% CI 1.57 to 5.51) were independently associated with a higher LARS score. Individuals with LARS had lower global QoL, functional subscales, and various symptom subscale scores. CONCLUSIONS: Our study identified the baseline prevalence of LARS in asymptomatic adults who have not undergone a low anterior resection. These normative data will allow for more accurate interpretation of ongoing studies on LARS in North American rectal cancer patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".