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Record W2110575502 · doi:10.1093/rheumatology/keg496

Measuring dyspepsia-related health in randomized trials: the Severity of Dyspepsia Assessment (SODA) and its use in treatment with NSAIDs and COX-2-specific inhibitors

2003· article· en· W2110575502 on OpenAlexaff
Linda Rabeneck

Bibliographic record

VenueBritish journal of rheumatology · 2003
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineTolerabilityCelecoxibValdecoxibClinical trialInternal medicineRandomized controlled trialPhysical therapyCyclooxygenaseAdverse effect

Abstract

fetched live from OpenAlex

Dyspepsia is a common problem that is important from the perspectives of both patient health and economics. While there has been variability in the definitions used to describe dyspepsia, there have also been few standardized outcomes tools designed to measure dyspepsia-related health, especially in relation to changes in dyspepsia over time. An evaluative tool was developed, the Severity of Dyspepsia Assessment (SODA), which takes into account the multidimensional nature of dyspepsia using three scales (Pain, Non-pain Symptoms, and Satisfaction with Dyspepsia-related Health) and demonstrates good psychometric properties with respect to validity, reliability and sensitivity to change in the measurement of dyspepsia-related health. Although originally developed for the assessment of uninvestigated dyspepsia, the validation of SODA for use in clinical trials suggested its ability to compare treatment effects of non-specific non-steroidal anti-inflammatory drugs (NSAIDs) and cyclooxygenase (COX)-2-specific inhibitors. In comparative trials of celecoxib or valdecoxib with non-specific NSAIDs, COX-2-specific inhibitors were demonstrated to have superior dyspepsia tolerability than non-specific NSAIDs. These data demonstrate that SODA is an effective instrument for measuring dyspepsia-related health with a broad range of applications.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.471
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.055
GPT teacher head0.296
Teacher spread0.241 · 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 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

Citations14
Published2003
Admission routes1
Has abstractyes

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