Plasma Low-Density Lipoprotein Cholesterol Estimated by Friedewald Compared to Martin-Hopkins Equation in Nigerian Population.
Bibliographic record
Abstract
BACKGROUND: Frailty has emerged as an important clinical measurement among older adults because of its negative health outcomes. OBJECTIVE: This study measured the prevalence and factors associated with frailty among older adults aged 60 years and above at a Geriatric Centre in Nigeria. METHODS: In this descriptive cross-sectional study, 971 older adults were recruited consecutively. Data on sociodemographics characteristics and clinical parameters were obtained using an interviewer-administered questionnaire and physical examination performed. The Frailty syndrome and Frailty Index were assessed using the Fried Frailty Criteria (FFC) and Canadian Study of Health and Aging (CSHA) scale respectively. Bivariate and multivariate analyses were carried out using SPSS version 21 at a p <0.05. RESULTS: The mean age of the participants was 71.3 (± 7.1) years with a female to male ratio of 2.4:1. Based on FFC scale, 498 older persons (51.3%) had frailty syndrome while only 148 (15.2%) were frail using the CSHA scale. The measure of agreement (Kappa statistics) was 0.22 (p<0001) indicating weak agreement between the two scales. Logistic regression analysis revealed increasing age (OR=1.948 [1.219-3.113]), multiple morbidities (OR= 1.584, [1.177-2.201]), depression (OR= 5.050, [2.501-9.442,]), imbalance or increased risk of fall (OR 1.623, [1.192-2.211,]), and inability to perform IADL (OR= 0.599 [0.535-0.670,]) to be the most significant determinants of frailty syndrome while obesity (OR=0.660, [0.449-0.971]), unusually appeared a deterrent. CONCLUSION: The prevalence of frailty syndrome was high among the older adults. Targeted and timely interventions on the modifiable factors may delay progression into frailty and the eventual negative health outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".