Frailty, sarcopenia, cachexia and malnutrition as comorbid conditions and their associations with mortality: a prospective study from UK Biobank
Bibliographic record
Abstract
BACKGROUND: Frailty, sarcopenia, cachexia and malnutrition are clinical conditions that share similar diagnostic criteria. This study aimed to investigate the clustering and mortality risk among these clinical conditions in middle- and older-aged adults. METHODS: 111 983 participants from UK Biobank were included. Sarcopenia was defined according to the EWGSOP 2019 while frailty using a modified version of the Fried criteria. Cachexia was defined using the Evans et al. classification and malnutrition using the Global Leadership Initiative on Malnutrition. The exposure variable was categorized as: no conditions; frailty only (one condition); frailty with sarcopenia (two conditions); frailty with ≥2 other conditions (three or four conditions). Its association with all-cause mortality was investigated using Cox-proportional hazard analysis. RESULTS: Frailty had the highest prevalence (45%) and was present in 92.1% of people with malnutrition and everyone with sarcopenia or cachexia. Compared with people with no conditions, those with frailty only and frailty with sarcopenia had higher risk of all-cause mortality. Individuals with frailty plus ≥2 other conditions had even higher risk (HR: 4.96 [95% CI: 2.73 to 9.01]). CONCLUSIONS: The four clinical conditions investigated overlapped considerably, being frailty the most common. The risk of all-cause mortality increased with the increasing number of conditions in addition to frailty.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".