Profiles of Met and Unmet Care Needs in the Oldest Old Primary Care Patients with Cognitive Disorders and Dementia: Results of the AgeCoDe and AgeQualiDe Study
Bibliographic record
Abstract
INTRODUCTION: The prevalence of mild cognitive impairment (MCI) and dementia is increasing as the oldest old population grows, requiring a nuanced understanding of their care needs. Few studies have examined need profiles of oldest old patients with MCI or dementia. Therefore, this study aimed to identify patients' need profiles. METHODS: The data analysis included cross-sectional baseline data from N = 716 primary care patients without cognitive impairment (n = 575), with MCI (n = 97), and with dementia (n = 44) aged 85+ years from the multicenter cohort AgeQualiDe study "needs, health service use, costs and health-related quality of life in a large sample of oldest old primary care patients [85+]". Patients' needs were assessed using the Camberwell Assessment of Needs for the Elderly (CANE), and latent class analysis identified need profiles. Multinomial logistic regression analyzed the association of MCI and dementia with need profiles, adjusting for sociodemographic factors, social network (Lubben Social Network Scale [LSNS-6]), and frailty (Canadian Study of Health and Aging-Clinical Frailty Scale [CSHA-CFS]). RESULTS: Results indicated three profiles: "no needs," "met physical and environmental needs," and "unmet physical and environmental needs." MCI was associated with the met and unmet physical and environmental needs profiles; dementia was associated with the unmet physical and environmental needs profile. Patients without MCI or dementia had larger social networks (LSNS-6). Frailty was associated with dementia. CONCLUSIONS: Integrated care should address the needs of the oldest old and support social networks for people with MCI or dementia. Assessing frailty can help clinicians to identify the most vulnerable patients and develop beneficial interventions for cognitive disorders. INTRODUCTION: The prevalence of mild cognitive impairment (MCI) and dementia is increasing as the oldest old population grows, requiring a nuanced understanding of their care needs. Few studies have examined need profiles of oldest old patients with MCI or dementia. Therefore, this study aimed to identify patients' need profiles. METHODS: The data analysis included cross-sectional baseline data from N = 716 primary care patients without cognitive impairment (n = 575), with MCI (n = 97), and with dementia (n = 44) aged 85+ years from the multicenter cohort AgeQualiDe study "needs, health service use, costs and health-related quality of life in a large sample of oldest old primary care patients [85+]". Patients' needs were assessed using the Camberwell Assessment of Needs for the Elderly (CANE), and latent class analysis identified need profiles. Multinomial logistic regression analyzed the association of MCI and dementia with need profiles, adjusting for sociodemographic factors, social network (Lubben Social Network Scale [LSNS-6]), and frailty (Canadian Study of Health and Aging-Clinical Frailty Scale [CSHA-CFS]). RESULTS: Results indicated three profiles: "no needs," "met physical and environmental needs," and "unmet physical and environmental needs." MCI was associated with the met and unmet physical and environmental needs profiles; dementia was associated with the unmet physical and environmental needs profile. Patients without MCI or dementia had larger social networks (LSNS-6). Frailty was associated with dementia. CONCLUSIONS: Integrated care should address the needs of the oldest old and support social networks for people with MCI or dementia. Assessing frailty can help clinicians to identify the most vulnerable patients and develop beneficial interventions for cognitive disorders.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".