COGNITIVE CHANGE AMONG NURSING HOME RESIDENTS; COGRISK-NH SCALE DEVELOPMENT TO PREDICT DECLINE
Bibliographic record
Abstract
Abstract Objectives Chronicle cognitive changes in nursing home residents; develop risk model identifying predictors of decline. Methods Using secondary analysis design with MDS data, cognitive status and change measures were calculated based on Cognitive Performance Scale (CPS). Baseline and quarterly follow-up analyses of US and Canadian interRAI data (n=1,257,832) were completed. Risk model from logistic regression analyses identified predictors of decline. Results Baseline 15% of residents were cognitively intact (CPS = 0); 11.2% borderline intact (CPS=1), 15% mild impairment (CPS = 2). 58.8% of residents fell into more severe CPS categories (3-6). Over time, increased proportion of residents declined – 17.1% at 6 months, 21.6% at 9 months, at 21 months, 34.0%. Over same time, more residents remained stable than declined. Baseline CPS score was strong predictor of decline. CPS categories 0-2 had 3-month decline rates in mid-teens, categories 3-5 had average decline rate of 9%. Two strata risk model construction was employed – one for CPS categories 0-2, second categories 3-5 and both were integrated into 6-category risk scale (CogRisk-NH). Mean decline rates at 3-month assessment ranged from 4.4% to 28.3%. Over time, distinction among risk categories continued – 6.9% to 38.4.% at 6 months, 16.2% to 61.4% at 21 months. Case distribution had 15.9% in category 1, 26.84% category 2, and 36.7% category 3. Three higher risk categories (4-6) represented 20.6% of residents. Conclusion CogRisk-NH scale differentiates among residents and likelihood of decline. Knowledge of risk for cognitive decline enables allocation of resources targeting amenable factors contributing to decline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".