Describing people with cognitive impairment and their complex treatment needs during routine care in the hospital – cross-sectional results of the intersec-CM study
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is an important determinant in health care. In the acute hospital setting cognition has a strong impact on treatment and care. Cognitive impairment can negatively affect diagnostics and treatment success. However, little is known about the individual situation and specific risks of people with cognitive impairments during hospital stays. The aim of the present research is to describe and analyze the treatment needs of people with cognitive impairments in acute hospital care. METHODS: The analyses use baseline data of the ongoing multisite, longitudinal, randomized controlled intervention trial intersec-CM (Supporting elderly people with cognitive impairment during and after hospital stays with Intersectoral Care Management), which recruited 402 participants at baseline. We assessed sociodemographic aspects, cognitive status, functional status, frailty, comorbidities, level of impairment, formal diagnosis of dementia, geriatric diagnoses, delirium, depression, pharmacological treatment, utilization of health care services and health care related needs. RESULTS: The sample under examination had been on average mildly cognitively impaired (MMSE M = 22.3) and had a mild to moderate functional impairment (Barthel Index M = 50.4; HABAM M = 19.1). The Edmonton Frail Scale showed a mean of 7.4 and half of the patients (52.3%) had been assigned a care level. About 46.9% had a geriatric diagnosis, 3.0% had a diagnosis of dementia. According to DSM-V 19.2% of the patients had at least one main symptom of depression. The mean number of regularly taken drugs per patient was 8.2. Utilization of health care services prior to the hospital stay was rather low. On average, the sample showed 4.38 care related needs in general, of which 0.60 needs were unaddressed at the time of assessment. CONCLUSIONS: Descriptive analyses highlight an in-depth insight into impairments and different care needs of people with cognitive impairments. The results emphasize the need for gender-specific analyses as well as an increased attention to the heterogeneity of needs of people with cognitive impairments related to specific wards, settings and regions where they are admitted. Our results indicate also that people with cognitive impairments represent a high proportion of older patients in acute hospital care. TRIAL REGISTRATION: The intersec-CM trial is registered at ClinicalTrials.gov ( NCT03359408 ).
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".