Impact of a Patient-centered Program for Low Anterior Resection Syndrome
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the impact of a low anterior resection syndrome (LARS) patient-centered program (LARS-PCP)-an informational and guided self-management intervention-on global quality of life (QoL) after surgery in comparison to standard care. BACKGROUND: Self-management using conservative measures is the cornerstone of LARS treatment; however, due to the individual and symptom-based nature of LARS, self-management largely consists of unguided troubleshooting with minimal success. METHODS: Adult patients who had undergone a restorative proctectomy with a diverting ostomy and who were scheduled for ostomy closure were randomized in a 1:1 ratio into 1 of 2 arms: LARS-PCP or standard care. The LARS-PCP consisted of an informational tool and nursing support centralized from one institution. Outcomes were measured with the use of patient-reported outcomes measures at various timepoints over the 12-month follow-up period. The primary outcome was global QoL at 6 months after ostomy closure. RESULTS: In total, 160 patients were randomized: 78 to the LARS-PCP and 82 to standard care. At 6 months after ostomy closure, LARS-PCP was associated with a higher mean global QoL (79.7 ±8.7 vs. 67.8 ±9.5, P= 0.001). This association was maintained at 12-month follow-up (82.0 ±9.1 vs. 74.9 ±10.1, P= 0.036). The incidence of major LARS was lower at 1-month (60.0% vs. 81.7%, P =0.008) postoperatively among LARS-PCP participants but was similar at 3, 6, and 12 months. CONCLUSIONS: This was the first multicenter randomized controlled trial to demonstrate that nurse-guided LARS self-management improved QoL after restorative proctectomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".