The Use of Prebiotic and Probiotic Interventions for Treating Gastrointestinal and Psychosocial Health Symptoms in Cancer Patients and Survivors: A Systematic Review
Bibliographic record
Abstract
Background: Cancer treatments can cause significant gastrointestinal (GI) health issues, and negatively affect patient’s psychosocial health and quality of life (QOL). Novel, integrative strategies using prebiotics and probiotics have been explored for treating adverse cancer treatment-related side effects. We evaluated the current literature for interventions using prebiotics or probiotics specifically to treat GI and psychosocial health issues in cancer patients and survivors. Methods: Five databases (PubMed, MEDLINE (Ovid), CINHAL, PsychINFO, Web of Science) were searched for studies with prebiotic or probiotic interventions where GI and/or psychosocial health outcomes were measured in adult cancer patients and survivors, and published before September 12th 2021. Results: Twelve studies (N = 974 participants) meeting the inclusion criteria were identified (randomized controlled trials [n = 10], single-group pre-post studies [n = 2]). Ten studies were conducted with patients on active cancer treatment, and 2 studies treated patients after anti-cancer therapies. Three studies used prebiotics, 7 studies used probiotics, and 2 studies used a combination therapy. The most commonly used probiotic strains were from the Lactobacillus genus. There was minimal evidence for prebiotics to improve GI or psychosocial health. Probiotics were associated with significant improvements in abdominal pain (n = 2), gas/bloating (n = 2), and especially diarrhea (n = 5), and with improvements in anxiety (n = 1), depression (n = 1), fatigue (n = 1), and QOL (n = 2). Conclusions: Studies specifically examining effects of prebiotics and probiotics on GI and psychosocial health outcomes are scarce. Probiotic intervention may improve some GI symptoms in cancer patients, and QOL in survivors. Controlled trials that consistently include GI and psychosocial health outcomes are needed.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".