The relationship between frailty, nutritional status, co-morbidity, CT-body composition and systemic inflammation in patients with COVID-19
Bibliographic record
Abstract
BACKGROUND: Frailty, determined by the Canadian Study of Health and Aging-Clinical Frailty Scale (CFS), is strongly associated with clinical outcomes including mortality in patients with COVID-19. However, the relationship between frailty and other recognised prognostic factors including age, nutritional status, obesity, sarcopenia and systemic inflammation is poorly understood. Therefore, the aim of this study was to examine the relationship between frailty and other prognostic domains, in patients admitted with COVID-19. METHODS: Patients who presented to our institutions between 1st April 2020-6th July 2020 with confirmed COVID-19 were assessed for inclusion. Data collected included general demographic details, clinicopathological variables, CFS admission assessment, Malnutrition Universal Screening Tool (MUST), CT-BC measurements and markers of systemic inflammation. RESULTS: 106 patients met the study inclusion criteria. The majority of patients were aged ≥ 70 years (67%), male (53%) and frail (scoring > 3 on the CFS, 72%). The majority of patients were not malnourished (MUST 0, 58%), had ≥ 1 co-morbidity (87%), were sarcopenic (low SMI, 80%) and had systemic inflammation (mGPS ≥ 1, 81%, NLR > 5, 55%). On multivariate binary logistics regression analysis, age (p < 0.01), COPD (p < 0.05) and NLR (p < 0.05) remained independently associated with frailty. On univariate binary logistics regression, NLR (p < 0.05) was significantly associated with 30-day mortality. CONCLUSION: Frailty was independently associated with age, co-morbidity, and systemic inflammation. The basis of the relationship between frailty and clinical outcomes in COVID-19 requires further study. Trial registration Registered with clinicaltrials.gov (NCT04484545).
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".