Frailty and Delirium in Older Adults: A Systematic Review and Meta‐Analysis of the Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES To evaluate the relationship between frailty and delirium. DESIGN Systematic review and meta‐analysis. SETTING MEDLINE, EMBASE, PubMed, Scopus, Web of Science, and Google Scholar databases were searched for articles on frailty and delirium published on or before October 31, 2017. PARTICIPANTS Individuals aged 65 and older. MEASUREMENTS Two authors independently reviewed all English‐language citations, extracted relevant data, and assessed studies for potential bias. Articles involving pediatric or neurosurgical populations, alcohol or substance abuse, psychiatric illness, head trauma, or stroke, as well as review articles, letters, and case reports were excluded. Studies underwent qualitative or quantitative analysis according to specified criteria. Using a random‐effects or fixed‐effects model, relative risk (RR) was calculated for the effect of frailty as a predictor of subsequent delirium. Heterogeneity was tested using Q and I 2 statistics. RESULTS We identified 1,626 articles from our initial search, of which 20 fulfilled the selection criteria (N=5,541 participants, mean age 77.8). Eight studies were eligible for meta‐analysis, showing a significant association between Q2 frailty and subsequent delirium (RR = 2.19, 95% confidence interval = 1.65–2.91). There was low variability among studies in the measures of association between frailty and delirium (I 2 2.24, p‐value Q‐statistic = .41) but high heterogeneity in the methods used to assess the two conditions. CONCLUSION This systematic review and meta‐analysis supports the existence of an independent relationship between frailty and delirium, although there is notable methodological heterogeneity between the methods used to assess the 2 conditions. Future studies are needed to better delineate the dynamics between these syndromes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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