Establishing the Minimal Clinically Important Difference for the Hospital Anxiety and Depression Scale in Patients With Cardiovascular Disease
Bibliographic record
Abstract
PURPOSE: The Hospital Anxiety and Depression Scale (HADS) is frequently used by clinicians to assess anxiety and depression in patients with cardiovascular disease; yet, its minimal clinically important difference (MCID) has not been established. The purpose of this study was to establish an MCID for the HADS in patients with cardiovascular disease. METHODS: A sample of 591 patients (74% male; ethnicity = 89% white; mean ± standard deviation [SD]: age = 63 ± 10 yr; and body mass index = 29.1 ± 5.6 kg/m) with cardiovascular disease enrolled in a 3-mo cardiac rehabilitation program were included in this study. The MCID for the HADS was estimated using distribution-based methods (ie, standard deviation, effect size, standard error of measurement, and minimal detectable change), anchor-based methods (ie, health transition question, correlation and linear regression, and receiver operating characteristic curve), and Delphi methodology (ie, clinical consensus). RESULTS: A total of 18 MCID values were calculated ranging from 0.81 to 5.21 (Anxiety subscale) and 0.5 to 5.57 (Depression subscale). The final MCID for the HADS, triangulated from the distribution-based, anchor-based, and Delphi-based findings, was 1.7 points. CONCLUSIONS: Our work provides the first estimates of an MCID by triangulating multiple methodologies for the HADS in patients with cardiovascular disease. This MCID may serve as an indicator of treatment success for clinicians and researchers and guide future interventions to improve the mental health of patients with cardiovascular disease.
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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 | 0.026 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".