Quality of life of patients with ulcerative colitis: Past, present, and future
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".