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Record W2160064793 · doi:10.1002/ibd.20301

Quality of life of patients with ulcerative colitis: Past, present, and future

2007· review· en· W2160064793 on OpenAlexaff
Jan E. Irvine

Bibliographic record

VenueInflammatory Bowel Diseases · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersShire
KeywordsMedicineQuality of life (healthcare)PsychosocialObservational studyTolerabilityDiseaseUlcerative colitisPopulationIntensive care medicineClinical trialCohortPhysical therapyInternal medicineAdverse effectPsychiatryNursing

Abstract

fetched live from OpenAlex

Quality of life (QoL) is vitally important to patients with chronic illnesses such as ulcerative colitis (UC) and has been assessed in observational, cross-sectional, and cohort studies. However, relatively few clinical trials have evaluated the QoL of patients with UC. Recently, greater availability of the necessary tools has facilitated the undertaking of studies showing that QoL of patients with UC is reduced significantly compared with that of the general population. Studies using disease-specific instruments have identified disease severity as the strongest predictor of QoL, with other disease-related predictors including type of medical or surgical treatment and the efficacy, tolerability, and acceptability to patients of particular types of medical or surgical treatments. Other factors, such as comorbid medical or psychosocial problems and adherence to treatment, also affect QoL. Combined use of generic and disease-specific instruments in clinical trials can ensure that all clinically relevant unexpected events (generic instrument) and important improvement or deterioration (disease-specific instrument) are captured. For accurate outcomes assessment, the use of comprehensively validated instruments is critical. The need for the development and evaluation of new instruments will be determined by the mechanisms and targets of novel therapies. Ultimately, QoL assessment of effective therapies will play a strong role in pharmacoeconomic evaluations, providing health policy makers with the evidence to support the treatments that can most effectively normalize QoL through complete symptom resolution, minimal side effects, and convenient administration.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.425
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.290
Teacher spread0.274 · 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.

Study designObservational
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".

Quick stats

Citations152
Published2007
Admission routes1
Has abstractyes

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