Bibliographic record
Abstract
PURPOSE: Psychological distress may decrease adherence to medical treatments and lead to poorer health outcomes of chronic diseases. The aim of this study was to evaluate the relationship between psychological distress and multimorbidity among patients seen in family practice after controlling for potential confounding variables and taking into account the severity of diseases. METHODS: We evaluated 238 patients to construct quintiles of increasing multimorbidity based on the Cumulative Illness Rating Scale (CIRS), which is a comprehensive multimorbidity index that takes into account disease severity. Patients completed a psychiatric symptom questionnaire as a measurement of their psychological distress. In the first model of logistic regression analyses, we used the counted number of chronic diseases as the independent variable. In subsequent models, we used the quintiles of CIRS. RESULTS: After adjusting for confounding factors, multimorbidity measured by a simple count of chronic diseases was not related to psychological distress (OR, 1.12; 95% CI, 0.97-1.29; P = .188), whereas multimorbidity measured by the CIRS remained significantly associated (OR, 1.67; 95% CI, 1.19-2.37; P = .002). The estimate risk of psychological distress by quintile of CIRS was as follows: Q1/2 = 1.0; Q3 = OR, 1.72; 95% CI, 0.53-5.86; Q4 = OR, 2.99; 95% CI, 1.01-9.74; Q5 = OR, 4.67; 95% CI, 1.61-15.16. CONCLUSIONS: Psychological distress increased with multimorbidity when we accounted for disease severity. Clinicians should be aware of the possible presence of psychological distress, which can further complicate the comprehensive management of these complex patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".