Predictive value of nutritional status and serological indicators in elderly patients with mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) in elderly individuals is a transitional stage between normal cognition and dementia. Understanding the risk factors for MCI and identifying those at high risk are extremely important for the elderly population. AIM: To analyze the risk factors for MCI in the elderly population and construct a clinical prediction model. METHODS: Total 295 elderly individuals presenting with memory loss diagnosed at Wuxi People's Hospital between March 2021 and March 2024 were included. Comprehensive demographic, clinical, and serological data were collected for analysis. Participants were categorized into either an MCI group or a normal group based on their performance on the Montreal Cognitive Assessment Scale. An elaborate clinical predictive model was developed to predict the likelihood of MCI in stroke patients; its accuracy was evaluated using area under curve values and calibration curves. RESULTS: The results of the study showed that old age, hypertension, diabetes, hyperlipidemia, smoking, high-salt diet, high-cholesterol diet, decreased red blood count, increased neutrophil lymphocyte ratio and increased low-density lipoprotein cholesterol were risk factors for the onset of MCI, with A high vitamin diet and elevated high-density lipoprotein cholesterol being protective factors. In addition, the prediction model constructed in this study exhibits good degrees of differentiation and calibration. CONCLUSION: The risk factors for MCI are diverse. Early identification of individuals at high risk of MCI can better intervene and improve their quality of life of MCI patients.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".