Delirium Index Six-Monthly in Patients with Dementia, Mild Cognitive Impairment and Subjective Cognitive Impairment: Keys to Interpreting Delirium Index in Cognitive Impairment
Bibliographic record
Abstract
Background : The delirium index (DI) is a simple non-copyrighted test which captures most delirium symptoms and signs. Despite its attractiveness it has been used in only 21/589 (3.6%) delirium articles published after DI appeared. Methods : Prospective observational cohort study in a geriatric memory clinic. We followed 259 community-dwelling elderly with dementia, mild cognitive impairment (MCI) and subjective cognitive impairment (SCI). Measurements: six-monthly DI, Mini-Mental State (MMSE), Montreal Cognitive Assessment (MoCA), Addenbrooke Cognitive Assessment (ACE-R), Frontal Assessment Battery (FAB) to predict the declines in instrumental activities of daily living (IADL). Mean follow-up was 622 days. Results : Mean DI increased from baseline 3.20 ± 1.90, to six-month 3.41 ± 2.00, twelve-month 3.61 ± 2.13, and peaked at eighteen-month 4.10 ± 2.2. It then declined to 3.71 ± 2.43 at twenty-four months, 3.98 ± 2.24 at thirty months. Spearman rank correlations were significant at a P < 0.0001 level between baseline DI and baseline and six-month IADL, MMSE, MoCA, ACE-R, FAB and with later DI at six, twelve, eighteen, twenty-four, thirty and thirty-six months. Comparing 227/259 patients with baseline DI 0 - 5 to 32/259 with baseline DI ≥ 6, the two groups differed significantly in baseline IADL (22% difference between means of the two groups, P = 0.004), baseline MMSE (35%, P < 0.001), baseline MoCA (48%, P < 0.001), baseline FAB (41%, P < 0.001), and baseline ACE-R (36%, P < 0.0001). Conclusions : Mean delirium index increased progressively every six months to eighteen months in a memory clinic. DI is a good tool to monitor elderly at risk for delirium. doi: http://dx.doi.org/10.4021/jnr204e
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".