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Record W4406734918 · doi:10.1111/nmo.15011

Consensus on Safe Initiation and Monitoring of Transanal Irrigation to Optimize Adherence With Therapy

2025· review· en· W4406734918 on OpenAlexaff
Anton Emmanuel, Klaus Krogh, B. Perrouin-Verbe, Andrei Krassiukov, S. M. P. Koch, Giovanni Mosiello, Gabriele Bazzocchi, Peter Christensen, Gianna M. Rodriguez, Concetta Brugaletta

Bibliographic record

VenueNeurogastroenterology & Motility · 2025
Typereview
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
Fundersnot available
KeywordsMedicineContraindicationIrrigationConstipationIntensive care medicineSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Transanal irrigation is a well-established minimally invasive therapy that addresses symptoms of both constipation and incontinence. The therapy has been extended from just neurogenic bowel dysfunction patients to those with disorders of brain-gut interaction and postsurgical conditions. AIM: To summarized the literature on transanal irrigation and update the contraindication profile. MATERIALS AND METHODS: We undertook a literature review of transanal irrigation complications and outcomes. RESULTS: Initiation of therapy as part of a bowel care regime is becoming more common outside specialist centers. In addition, the concept of both high- and low-volume irrigation schedules has entered the treatment paradigm, and it is clear that there is a differing safety profile. We present an update from the previous long list of contraindications. DISCUSSION: We describe how optimizing long-term adherence depends on these factors in addition to a structured follow-up programme. CONCLUSION: Transanal irrigation is an increasingly used therapy, with a good safety profile, further improved by the advent of low-volume irrigation options. Key to safe and effective usage of transanal irrigation is careful patient selection allied to tailored initial training of the patient.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.756
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.343
Teacher spread0.280 · 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.

Study designObservational
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

Citations8
Published2025
Admission routes1
Has abstractyes

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