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Record W4398782378 · doi:10.7759/cureus.60962

Integrated Approaches in the Management of Gastrointestinal Disorders: A Biopsychosocial Perspective

2024· article· en· W4398782378 on OpenAlexaff
Muhammad Aamir Chughtai, Munara K Kerimkulova, Omid Mushtaq, Vibhavari Hagenahalli Anand, Abdur Rehman, Abdullah Shehryar, B. Hassan, Rabia Islam, Hamza Islam, Muzafar Mansoor, Shehryar Rehman

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsASTER
Fundersnot available
KeywordsBiopsychosocial modelPerspective (graphical)PsychotherapistMedicinePsychologyIntensive care medicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Gastrointestinal (GI) disorders, including gastroesophageal reflux disease (GERD), inflammatory bowel disease (IBD), gastritis/peptic ulcer disease (PUD), and celiac disease, significantly impact global health and economic stability. This review synthesizes current literature to elucidate the pathophysiology, clinical manifestations, diagnostic challenges, and management strategies of these prevalent conditions. Through a biopsychosocial lens, we examine the role of the gut microbiome in disease modulation and explore innovative therapeutic advancements, including microbiome-targeting interventions. The review highlights the necessity of a multidisciplinary approach to patient care, integrating medical treatment with dietary, psychological, and lifestyle modifications. By addressing these disorders holistically, the article aims to foster a deeper understanding of their biopsychosocial impacts and encourage more effective, patient-centered treatment paradigms. The findings underscore the imperative for continued research and interdisciplinary collaboration to enhance patient outcomes and reduce healthcare burdens associated with GI disorders.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.303
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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