Quality of life: a potentially useful measure to indicate subclinical flares in Crohn disease
Bibliographic record
Abstract
BACKGROUND: While quality of life (QoL) is a well-recognised outcome measure of Crohn disease (CD) activity, its influence on other outcome measures, including exacerbation of CD is poorly understood. If QoL measures were to be associated with intestinal inflammatory activity, they might be useful for early detection of subclinical flares. AIMS: We hypothesised that low QoL might be associated with subsequent CD flares. METHODS: A cohort of 318 adult CD patients was observed for 1 year after assessment of baseline characteristics. Data were collected in Swiss university hospitals, regional hospitals and private practices. At inclusion, patients completed the Inflammatory Bowel Disease QoL Questionnaire (gastrointestinal QoL; range: 32 to 224 points) and the Short Form-36 Health Survey (general QoL; range: 35 to 145 points). During follow up, flares were recorded. Binary logistic regression was performed to estimate the relation between QoL and the odds of subsequent flares. RESULTS: A twofold decrease in the odds of flares (99% CI: 1.1; 4.0) per standard deviation of gastrointestinal QoL and a threefold decrease (99% CI: 1.5; 6.2) per standard deviation of general QoL were observed. CONCLUSIONS: The close association between QoL and subsequent flares suggests that QoL measures might be useful in detecting upcoming flares before they become clinically apparent.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".