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 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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".