Bibliographic record
Abstract
The concept of frailty was proposed in 1979 to capture heterogeneity in the health status and mortality risk of people of the same chronological age (1). Clinically, frailty is generally considered a state of increased vulnerability to adverse health outcomes, although there is some controversy about the best way to assess it (2). The two most commonly used tools to quantify frailty in a clinical setting are the “frailty index” approach (3), and the “frailty phenotype” approach (4). A frailty index measures the number of health-related deficits a person has accumulated over their lifetime. The number of deficits present is divided by the number of deficits measured, to give a frailty index score between 0 and 1. The frailty phenotype determines if a person is frail based on poor performance in five functional criteria (weight loss, exhaustion, weakness, slowness, lack of activity). If a person has poor performance on one or two of these criteria they are considered pre-frail, and if they have poor performance on three or more they are considered frail. Frailty is an important clinical challenge, as the population ages and the number of frail individuals in the health care system rises.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".