Assessment of health-related quality of life of patients with inflammatory bowel diseases in Eastern Province, Saudi Arabia
Bibliographic record
Abstract
Inflammatory bowel disease patients have impaired quality of life with physical, social and emotional dysfunction. This project aimed to assess the effects of socio-demographic and clinical variables on quality of life and to identify its predictors. In a cross-sectional paper-based study, 50 consecutive non-selected patients attending the teaching hospital completed a disease-specific McMaster quality of life tool. Socio-demographic and clinical data were collected from patients' records. The t-test and Mann-Whitney test were used to determine the probability of significant differences between quality of life domains and independent variables. Multiple linear regression was used to determine quality of life predictors. Younger and highly educated patients had higher social scores. Those with shorter disease durations had higher systemic scores. Patients in remission had higher systemic, social, bowel and overall scores. Relapse was a significant predictor of decreased systemic, social, bowel and overall scores. Long disease duration was a significant predictor of decreased systemic and overall scores. Younger age at disease onset was a significant predictor of decreased emotional score. However, high education was a significant predictor of improved social score. Relapse, long disease duration, low education and young age at disease onset were associated with low quality of life. Prospective studies should investigate how interventions addressing these predictors may lead to improved quality of life.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".