The Development of a Model to Predict Cognitive Decline Within 12 Months in Home Care Clients
Bibliographic record
Abstract
AIM: To develop and validate a model to predict cognitive decline within 12 months for home care clients without a diagnosis of dementia. DESIGN: We included all adults aged ≥ 18 years who had at least two interRAI Home Care assessments within 12 months, no diagnosis of dementia and a baseline Cognitive Performance Scale score ≤ 1. The sample was randomly split into a derivation cohort (75%) and a validation cohort (25%). Significant cognitive decline was defined as an increase (deterioration) in Cognitive Performance Scale scores from '0' or '1' at baseline to a score of ≥ 2 at the follow-up assessment. METHODS: Using the derivation cohort, a multivariable logistic regression model was used to predict cognitive decline within 12 months. Covariates included demographics, disease diagnoses, sensory and communication impairments, health conditions, physical and social functioning, service utilisation, informal caregiver status and eight interRAI-derived health index scales. The predicted probability of cognitive decline was calculated for each person in the validation cohort. The c-statistic was used to assess the model's discriminative ability. This study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelines. RESULTS: A total of 6796 individuals (median age: 82; female: 60.4%) were split into a derivation cohort (n = 5098) and a validation cohort (n = 1698). Logistic regression models using the derivation cohort resulted in a c-statistic of 0.70 (95% CI 0.70, 0.73). The final regression model (including 21 main effects and 8 significant interaction terms) was applied to the validation cohort, resulting in a c-statistic of 0.69 (95% CI 0.66, 0.72). CONCLUSION: interRAI data can be used to develop a model for identifying individuals at risk of cognitive decline. Identifying this group enables proactive clinical interventions and care planning, potentially improving their outcomes. While these results are promising, the model's moderate discriminative ability highlights opportunities for improvement.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".