Management of Opioid-Induced and Non–Opioid-Related Constipation in Patients With Cancer: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PROBLEM IDENTIFICATION: A systematic review and meta-analysis was conducted to inform the development of national clinical practice guidelines on the management of cancer constipation. LITERATURE SEARCH: PubMed®, Wiley Cochrane Library, and CINAHL® were searched for studies published from May 2009 to May 2019. DATA EVALUATION: Two investigators independently reviewed and extracted data from eligible studies. The Cochrane Collaboration risk-of-bias tool was used, and the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach was used to assess the certainty of the evidence. SYNTHESIS: For patients with cancer and opioid-induced constipation, moderate benefit was found for osmotic or stimulant laxatives; small benefit was found for methylnaltrexone, naldemedine, and electroacupuncture. For patients with cancer and non-opioid-related constipation, moderate benefit was found for naloxegol, prucalopride, lubiprostone, and linaclotide; trivial benefit was found for acupuncture. IMPLICATIONS FOR PRACTICE: Effective strategies for managing opioid-induced and non-opioid-related constipation in patients with cancer include lifestyle, pharmacologic, and complementary approaches. SUPPLEMENTAL MATERIAL CAN BE FOUND AT HTTPS: //bit.ly/3c4yewT.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.028 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".