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Predictors of Quality of Life (QOL) in Functional Dyspepsia (FD)

2006· article· en· W2979072462 on OpenAlexaboutno aff
Jason Bratten, Laurie Keefer

Bibliographic record

VenueThe American Journal of Gastroenterology · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychosocialStepwise regressionBiopsychosocial modelQuality of life (healthcare)Bayesian multivariate linear regressionAlexithymiaInternal medicinePhysical therapyLinear regressionClinical psychologyPsychiatry

Abstract

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Purpose: QOL is a commonly used outcome measure in FGID. Perceptions of QOL can be affected by physical health, psychological state, personal beliefs, social relationships and their interactions. Given the complex biopsychosocial nature of FGID, we sought to evaluate the relationship between QOL, physical symptoms, gastric function, and psychosocial characteristics in patients with FD. Methods: We performed a retrospective analysis of pts with Rome II FD who completed general (SF-36) and dyspepsia-specific (Nepean Dyspepsia Index (NDI)) QOL measures. Pts also completed a 15-item dyspepsia symptom score (DSS), psychosocial battery and a 5-min water load test (WL). Psychosocial measures included the SCL-90-R, Toronto Alexithymia Scale (TAS-20) and the Somatosensory Amplification Scale (SSAS). 30 pts also completed a 13C-S. platensis solid phase gastric emptying breath test. Dependent variables were the NDI score and the Mental and Physical Composite Summaries of the SF-36 (MCS and PCS). Candidate independent variables that significantly correlated with dependent variables were entered into stepwise regression. Stepwise regression was also performed using entered independent variables to predict individual subscales of the SF-36. Results: 151 pts were studied and included 117F/34M with a mean(SDEV) age of 39 ± 13years. Variables significantly correlated with QOL included sex, WL, DSS, SCL90, the Difficulty Identifying Feelings (DIF) scale of the TAS-20 and SSAS scores. Both SF-36 MCS and PCS were explained using single step models. PCS was predicted by DSS (r2= 0.16; p= 0.008) while MCS was predicted by the SCL-90 global severity index (GSI) (r2= 0.334; p < 0.0001). NDI was predicted using a 3-step model that included DSS (r2= 0.25; p= 0.001), DSS+WL (r2= 0.33; p < 0.0001) and DSS+WL+ DIF (r2= 0.43; p < 0.0001). Physical function and bodily pain scores were predicted by DSS alone (r2= 0.115 and 0.24) while GSI predicted physical role (r2= 0.118), mental health (r2= 0.54), emotional role (r2= 0.15), vitality (r2= 0.29) and general health (r2= 0.16). Social function was predicted by a two-step model including GSI and WL (r2= 0.37). Conclusions: QOL in FD is largely determined by interactions between symptom severity, psychiatric distress and alexithymic traits. These data highlight the complex nature of QOL as an outcome measure and also demonstrate that symptoms alone are not sole predictors of QOL even for condition specific measures such as the NDI. If QOL is to truly be viewed as an outcome measure in FGID, a broader biopsychosocial approach is needed.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.385
Teacher spread0.347 · 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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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