Meeting complex multidimensional needs in older patients and their families during and beyond critical illness
Bibliographic record
Abstract
PURPOSE OF REVIEW: To highlight the emerging crisis of critically ill elderly patients and review the unique burden of multidimensional morbidity faced by these patients and caregivers and potential interventions. RECENT FINDINGS: Physical, psychological, and cognitive sequelae after critical illness are frequent, durable, and robust across the international ICU outcome literature. Elderly patients are more vulnerable to the multisystem sequelae of critical illness and its treatment and the resultant multidimensional morbidity may be profound, chronic, and significantly affect functional independence, transition to the community, and quality of life for patients and families. Recent data reinforce the importance of baseline functional status, health trajectory, and chronic illness as key determinants of long-term functional disability after ICU. These risks are even more pronounced in older patients. SUMMARY: The current article is an overview of the outcomes of older survivors of critical illness, putative interventions to mitigate the long-term morbidity of patients, and the consequences for families and caregivers. A multimodal longitudinal approach designed to follow patients for one or more years may foster a better understanding of multidimensional morbidity faced by vulnerable older patients and families and provides a detailed understanding of recovery trajectories in this unique population to optimize outcome, goals of care directives, and ongoing informed consent to ICU treatment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".