ONS Guidelines™ for Opioid-Induced and Non–Opioid-Related Cancer Constipation
Bibliographic record
Abstract
PURPOSE: This evidence-based guideline intends to support clinicians, patients, and others in decisions regarding the treatment of constipation in patients with cancer. METHODOLOGIC APPROACH: An interprofessional panel of healthcare professionals with patient representation prioritized clinical questions and patient outcomes for the management of cancer-related constipation. Systematic reviews of the literature were conducted. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach was used to assess the evidence and make recommendations. FINDINGS: The panel agreed on 13 recommendations for the management of opioid-induced and non-opioid-related constipation in patients with cancer. IMPLICATIONS FOR NURSING: The panel conditionally recommended a bowel regimen in addition to lifestyle education as first-line treatment for constipation. For patients starting opioids, the panel suggests a bowel regimen as prophylaxis. Pharmaceutical interventions are available and recommended if a bowel regimen has failed. Acupuncture and electroacupuncture for non-opioid-related constipation are recommended in the context of a clinical trial. SUPPLEMENTARY MATERIAL CAN BE FOUND AT HTTPS: //bit.ly/30y29sI.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".