The External Validation of the Nursing Homes Short Depression Inventory in Older Adults with Major Neurocognitive Disorders in Long-Term Care Centers
Bibliographic record
Abstract
INTRODUCTION: Depression is often difficult to detect in long-term care (LTC) patients with major neurocognitive disorders (MNCD), and an observer-rated screening scale could facilitate assessments. This study aimed to establish the external validity and reliability of the Nursing Homes Short Depression Inventory (NH-SDI) in LTC patients with MNCD and to compare its estimates to the Cornell Scale for Depression in Dementia (CSDD), the most used scale for depression in MNCD. METHODS: A focus discussion group of experts assessed the content validity of the NH-SDI. Then, a convenience sample of 93 LTC patients with MNCD was observer-rated by trained nurses with the NH-SDI and CSDD. For 57 patients, a medical assessment of depression was obtained, and screening accuracy estimates were generated. RESULTS: The prevalence of depression was 8.8% as per reference standard. NH-SDI's content validity was judged acceptable with minor item wording modifications and specifications. The NH-SDI (cut-off ≥3) achieved 100% (95% confidence interval [CI]: 46-100%) sensitivity, 83% (95% CI: 69-91%) specificity, and 36% (95% CI: 14-64%) positive predictive value (PPV). The CSDD (cut-off ≥3) achieved 100% (95% CI: 46-100%) sensitivity, 75% (95% CI: 61-86%) specificity, and 28% (95% CI: 11-54%) PPV. No significant differences in areas under the receiver operating characteristic curve were found between scales. The NH-SDI and CSDD were highly correlated (rs = 0.913; p < 0.001) and reliable (ICC = 0.77; p < 0.001). CONCLUSION: The NH-SDI appears valid and reliable in LTC patients with MNCD and quicker than the CSDD to rule out depression in a busy or short-staffed setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".