Frailty, sarcopenia, cachexia and malnutrition as comorbid conditions and their associations with mortality: a prospective study from UK Biobank
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
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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