Models of Care in Geriatric Intensive Care—A Scoping Review on the Optimal Structure of Care for Critically Ill Older Adults Admitted in an ICU
Why this work is in the frame
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Bibliographic record
Abstract
A growing proportion of critically ill patients admitted in ICUs are older adults. The need for improving care provided to older adults in critical care settings to optimize functional status and quality of life for survivors is acknowledged, but the optimal model of care remains unknown. We aimed to identify and describe reported models of care. DATA SOURCES: We conducted a scoping review on critically ill older adults hospitalized in the ICU. Medline (PubMed), Embase (OvidSP), Cumulative Index to Nursing and Allied Health Literature (Ebsco), and Web of Science (Clarivate) were searched from inception to May 5, 2020. STUDY SELECTION: We included original articles, published abstracts, review articles, editorials, and commentaries describing or discussing the implementation of geriatric-based models of care in critical care, step-down units, and trauma centers. The organization of care had to be described. Articles only discussing geriatric syndromes and specific interventions were not included. DATA EXTRACTION: Full texts of included studies were obtained. We collected publication and study characteristics, structures of care, human resources used, interventions done or proposed, results, and measured outcomes. Data abstraction was done by two investigators and reconciled, and disagreements were resolved by discussion. DATA SYNTHESIS: Our search identified 3,765 articles, and we found 19 reporting on the implementation of geriatric-based models of care in the setting of critical care. Four different models of care were identified: dedicated geriatric beds, geriatric assessment by a geriatrician, geriatric assessment without geriatrician, and a fourth model called "other approaches" including geriatric checklists, bundles of care, and incremental educational strategies. We were unable to assess the superiority of any model due to limited data. CONCLUSIONS: Multiple models have been reported in the literature with varying degrees of resource and labor intensity. More data are required on the impact of these models, their feasibility, and cost-effectiveness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it