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Record W3007992477 · doi:10.1093/jcag/gwz047.229

A230 GAPS IN IDENTIFYING FOOD-RELATED QUALITY OF LIFE AND HYPERVIGILANCE IN AT-RISK INDIVIDUALS WITH IBD: REVIEW OF VALIDATED SCREENING TOOLS

2020· article· en· W3007992477 on OpenAlexaff
Alexa N. Sasson, Laura E. Targownik, Kathy Vagianos, C N Bernstein

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsUniversity of ManitobaUniversity of Toronto
Fundersnot available
KeywordsHypervigilancePsycINFOMedicinePsychosocialAnxietyMEDLINECochrane LibraryDiseaseQuality of life (healthcare)Systematic reviewMalnutritionClinical psychologyMeta-analysisPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Inflammatory bowel disease (IBD) has a profound impact on psychosocial aspects of food and nutrition, thereby affecting food-related quality of life (QoL). While there is literature on associated prevalence of increased anxiety in individuals with IBD, there is limited data on its contribution to hypervigilance and orthorexia with food intake. Many patients with IBD have strong beliefs about dietary symptom triggers, which can lead to avoidance behaviors and decreased nutrient intake. This can exacerbate malnutrition and disordered eating, as well as increase disease-related stress and negatively impact coping. While there are validated scales evaluating anxiety-related, QoL-related and food-related behaviors in IBD, a combined screening tool to assess this comorbid axis is not well described. Aims To conduct a systematic review of existing literature in order to inform practice and facilitate development of an effective food-related hypervigilance and QoL evaluation in IBD patients Methods The literature was systematically searched through September 2019, using an electronic database querying Embase, PubMed, MEDLINE, Cochrane Library and PsycINFO. We searched original articles describing development, validation and measurement properties for screening tools on anxiety, QoL and food-related behaviors in IBD from 1975 to 2018. The primary outcome of interest was to evaluate the current measurements of the validated tools to identify whether a screening tool highlighting all above parameters exists for patients with IBD. Results Initial database search resulted in 5548 articles. After screening titles and abstracts, 168 were included. After full text review and deduplication, 23 validated scales were identified for use in IBD patients with respect to measuring anxiety, health-related QoL and food-related behaviors. There was substantial heterogeneity in IBD populations using the assessment tools (adult vs. pediatric, CD vs. UC, inpatient vs. outpatient). The breakdown of studies identified: 2 studies (8.6%) evaluated QoL and anxiety, 2 studies (8.6%) evaluated QoL and food-related behaviors. The remainder of studies individually assessed QoL, anxiety and food-related behaviors (47.8%, 26% and 8.6% respectively). None of the tools performed satisfactory to establish all three measurements in individuals with IBD. Conclusions Recent evidence suggests the presence of dietary hypervigilance in individuals with chronic GI conditions potentially leading to food restrictive behaviors impacting QoL. Screening models evaluating multivariable relations of anxiety in food-related behaviors and QoL in IBD is lacking. Efforts should be made to develop and validate a multi-assessment screening tool to aid in early identification of this prevalence in IBD patients to facilitate improved management outcomes. Funding Agencies 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)0.016
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.072
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0200.018
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.283
Teacher spread0.237 · 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 designSystematic review
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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