Bibliographic record
Abstract
The terms successful aging (SA) and frailty appear to have much in common, both in terms of overlapping constructs and common challenges with consensus and operationalization. The aim of this review is to summarize existing literature that defines that relationship. Primary and secondary source articles that used either term in the title or abstract were systematically reviewed for relevance to the study objective. Of 61 articles that met these criteria, 30 were secondary source, and of these four were highly relevant. Four of the remaining 31 original research articles were selected, and the prevalence of frailty and SA in populations with different characteristics were described and compared. The same model of frailty was used in all primary studies, but definitions for successful aging were heterogeneous. The prevalence of frailty ranged from 11.8% to 44.0% and that of SA ranged from 10.4% to 47.2%. The definitions used for each, especially the extent of multidimensionality, appeared to reflect the degree of overlap between SA and frailty. Whether frailty and SA are part of the same or different constructs, there is a pressing need for an ordered taxonomy to advance research that translates into clinical practice.
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.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".