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Record W4388452651 · doi:10.3390/jcm12226971

Neurogenic Bowel Dysfunction in Patients with Spinal Cord Injury and Multiple Sclerosis—An Updated and Simplified Treatment Algorithm

2023· review· en· W4388452651 on OpenAlexaff
Fredrika Magnuson, Peter Christensen, Andrei V. Krassioukov, Gianna M. Rodriguez, Anton Emmanuel, Steven Kirshblum, Klaus Krogh

Bibliographic record

VenueJournal of Clinical Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicSpinal Dysraphism and Malformations
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersColoplast
KeywordsMedicineConstipationMultiple sclerosisFecal incontinenceAlgorithmSpinal cord injuryQuality of life (healthcare)Pyramid (geometry)Spinal cordRectumInternal medicineSurgeryPsychiatry

Abstract

fetched live from OpenAlex

Neurogenic bowel dysfunction (NBD) is a common condition in individuals with spinal cord injury (SCI) or multiple sclerosis (MS). It usually entails constipation, difficult evacuation of the rectum, and fecal incontinence (FI); often in combination. It is highly burdensome for affected patients and is correlated with poor quality of life. The current treatment algorithm, or treatment pyramid, does not completely correspond to actual clinical practice, and the known and classical pyramid contains both treatments still in their experimental stage as well as several treatments which are not available at all treatment centers. Thus, an updated treatment algorithm is called upon, and the authors of this paper therefore propose a simplified version of the treatment pyramid, aiming to guide clinicians in treating NBD.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.181
GPT teacher head0.438
Teacher spread0.257 · 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

Citations24
Published2023
Admission routes1
Has abstractyes

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