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Record W4386995533 · doi:10.3390/jcm12196148

Management of Constipation in Hospitalized Patients

2023· review· en· W4386995533 on OpenAlexfundno aff
Gregory S. Sayuk, Qi Yu, Corey Shy

Bibliographic record

VenueJournal of Clinical Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsnot available
FundersSalix PharmaceuticalsBausch HealthProgenics Pharmaceuticals
KeywordsMedicineConstipationLaxativeOpioidIntensive care medicineFunctional constipationPhysical examinationChronic constipationPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Constipation is frequently encountered in hospital settings and can have potentially serious consequences yet is often underrecognized and undertreated. Opioid-induced constipation is a common cause of constipation in hospitalized patients. Opioids induce constipation through agonistic effects on enteric µ-opioid receptors. This review aims to provide insight on the identification and management of constipation in inpatient settings, with a particular focus on opioid-induced constipation. Constipation assessment should be routinely initiated at hospital admission and can be facilitated by thorough symptom assessments; relevant patient history, including recent medication use; physical examination; and patient assessment tools developed to evaluate the impact of constipation. Management of opioid-induced constipation should begin with ensuring adequate hydration and electrolyte balance and encouraging patient mobilization. Other treatments may include laxatives, enemas, intestinal secretagogues, peripherally acting µ-opioid receptor antagonists, and manual disimpaction. Surgical intervention may be required for some patients as a salvage therapy in severe, refractory cases.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.216
GPT teacher head0.517
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2023
Admission routes1
Has abstractyes

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