Transitions and trajectories in intrinsic capacity states over time: a systematic review
Bibliographic record
Abstract
Intrinsic Capacity (IC) is a crucial measure of the comprehensive physiological and psychological capabilities of older adults, playing a key role in assessing healthy aging. This systematic review aims to explore the trajectories of IC in older adults, as well as the associated determinants and health outcomes. By searching through PubMed, Embase, Ovid, and Web of Science databases, we identified 13 studies that met our inclusion criteria. To ensure the rigor of the review, the Newcastle-Ottawa Scale (NOS) critical appraisal tool for cohort studies and the Guidelines for Reporting on Latent Trajectory Studies were employed to assess the quality of the studies included. When IC is represented as a single composite value, there are primarily three trajectory types: declining trajectory (characterized by a sharp, moderate, or mild decline from baseline IC), stable trajectory (little change compared to baseline IC), and high trajectory (high baseline IC with an increasing trend). When IC is broken down into individual dimensions, these trajectories primarily reflect the degree of impairment in different domains and changes in IC status. The trajectories can be divided into robust status (no impaired domains, stable IC status), mild impairment (impairment in 1-2 domains, mild IC impairment), and severe impairment (impairment in multiple domains, severe IC impairment). Factors influencing IC trajectories include age, gender, education level, ethnicity, number of chronic diseases, marital status, perceived financial adequacy, economic assistance status, self-assessed health status, and inflammatory biomarkers (such as IL-6, TNFR-1, and GDF-15). Adverse IC trajectory patterns are associated with increased mortality, quality of life, disability, frailty, and fall risk. Future research should focus on changes in IC at the end of life, increase the number of assessment time points, use objective measurement methods, and consider experimental designs to better understand the mechanisms behind IC trajectories, providing a scientific basis for targeted interventions.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".