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Record W2790904335 · doi:10.1093/jcag/gwy009.112

A112 THE VALIDITY OF PATIENT-LED SELF-SCREENS FOR IDENTIFYING MALNUTRITION IN INFLAMMATORY BOWEL DISEASE

2018· article· en· W2790904335 on OpenAlexaffabout
Tannaz Eslamparast, Kamal Farhat, Lorian Taylor, Nusrat Shommu, Ashish Kumar, Quinn Fitzgerald, Karen I. Kroeker, M Raman, Puneeta Tandon

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2018
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineMalnutritionUlcerative colitisInflammatory bowel diseaseReferralPopulationDiseaseCrohn's diseaseGold standard (test)Internal medicinePhysical therapyPediatricsFamily medicine

Abstract

fetched live from OpenAlex

Malnutrition is common in Inflammatory Bowel Disease (IBD) and is associated with significant morbidity and mortality. Identification of high-risk patients using an efficient, and sensitive screen is the first step to dietitian referral for nutritional assessment and intervention. To determine the validity of patient led self-screens against a dietitian-led subjective global assessment (SGA) to detect malnutrition in IBD patients. Adult patients were prospectively recruited from IBD clinics in Edmonton and Calgary. Patients completed 4 self-screening questionnaires: abridged Patient-generated Subjective Global Assessment (abPG-SGA), Malnutrition Universal Screening Tool (MUST), Canadian Nutrition Screening Tool (CNST) and Malnutrition Screening Tool (MST). A dietitian blinded to the results of the screens carried out a gold standard nutritional assessment using the SGA. A total of 95 IBD patients (60 Crohn’s (CD) and 35 Ulcerative colitis (UC)), 52% male were assessed. According to Harvey-Bradshaw Index and partial Mayo scores, 14% of CD and 28% of UC patients had moderate to severe disease. The most common symptoms affecting dietary intake in this patient population were diarrhea (33%), pain (32%), poor appetite (24%) and fatigue (22%). According to the dietitian –led SGA, 21% of patients (15% Crohn’s, 31% UC) were moderately to severely malnourished. Patients classified themselves at moderate to high risk of malnutrition in 50% of cases (abPG-SGA), 37% (MUST), 15% (CNST), 21% (MST). Of the 4 screening tools, the abPG-SGA had the best test characteristics (see Table 1). The abPG-SGA is a promising nutrition screening tool in patients with IBD. It is time-efficient and can be completed by patients in the waiting room. With the high sensitivity and high negative predictive value for malnutrition detection, all patients who screened at risk of malnutrition would be appropriately referred for further assessment. This tool has been successfully utilized in other chronic disease populations. Future clinical practice should integrate the abPG-SGA into routine IBD nutrition screening. Measures of validity of the abPG-SGA, MUST, CNST and MST against the dietitian-administered SGA in IBD abPG-SGA abridged patient generated subjective global assessment, MUST malnutrition universal screening tool, CNST Canadian nutrition screening tool, MST malnutrition screening tool, SGA subjective global assessment, PPV positive predictive value, NPV negative predictive value None

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)0.013
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.291
Teacher spread0.267 · 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 designObservational
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".

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Citations1
Published2018
Admission routes2
Has abstractyes

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