Is Fibre an Effective Strategy to Improve Laxation in Long-Term Care Residents?
Bibliographic record
Abstract
The high prevalence of constipation in long-term care (LTC) residents has been a long-standing issue for caregivers, attending health professionals, and the residents themselves. The traditional medical response has been to utilize pharmaceutical laxatives, enemas, and suppositories for treatment. The purpose of this review was to determine if fibre supplementation (including fibre added to foods) is effective in increasing stool frequency, improving stool consistency, and decreasing laxative use in LTC residents. A systematic search was conducted using PubMed and CINAHL databases, inclusive to March 2017. Search terms included: "long-term care" or "nursing home" AND "fiber (fibre)," "bran," "psyllium," "inulin," or "prebiotic." Intervention trials of fibre supplementation with ≥5 LTC residents were included. The search generated 456 articles following removal of duplicates; 8 studies met the inclusion criteria. Three additional trials were identified through a hand search of references of pertinent articles. Current evidence suggests that added fibre may be effective in increasing stool frequency and/or decreasing laxative use in LTC residents and, thus, may lessen the burden of constipation. However, randomized controlled trials are needed to clearly demonstrate the effects of adding fibre to foods, particularly insoluble and less fermentable sources, on constipation in LTC residents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".