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Record W3092700085 · doi:10.1188/20.onf.671-691

ONS Guidelines™ for Opioid-Induced and Non–Opioid-Related Cancer Constipation

2020· article· en· W3092700085 on OpenAlexaff
Barbara Rogers, Pamela Ginex, Allison B. Anbari, Brian Hanson, Kristine B. LeFebvre, Rachael Lopez, Deborah M. Thorpe, Brenda Wolles, Kerri Moriarty, Christine Maloney, Mark Vrabel, Rebecca L. Morgan

Bibliographic record

VenueOncology nursing forum · 2020
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsConstipationMedicineFeelingOpioidIntensive care medicineFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.094
GPT teacher head0.407
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2020
Admission routes1
Has abstractyes

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