Correlation between cognitive impairment of patients with mild cognitive impairment and high sensitive C-reactive protein
Bibliographic record
Abstract
Objective To investigate the correlation between cognitive impairment risk and high sensitive C-reactive protein(hs-CRP) in the elderly people with mild cognitive impairment(MCI) in Beijing.Methods Population-based cross-sectional study was carried on residents over 50 years old(including 50 years old) who came from four communities of Haidian district and three natural villages of Yufa town in Daxing district,initially,to test subjects' cognitive function with MMSE;then,to evaluate the subjects who were abnormal on the MMSE with ADL,CDR and Hachinski ischemia index according to the standard of illiterate group≤19 scores,primary-school graduate group≤22 scores,middle-school graduate and above group≤26 scores;eventually,to screen out the patient with MCI according to medical history and cognitive function scores,to detect the serum hs-CRP of all subjects,to compare the various indicators between MCI group and normal cognitive function group,to analyze the correlation between hs-CRP level and MCI risk,and the correlation between hs-CRP and cognitive score on orientation,memory,attention,calculation and language ability of MCI patients.Results There were 3127 people [including 1518 people in rural areas(48.5%),1609 people in city(51.5%)] were included in the study,among which 62 cases were diagnosed MCI,25 cases were diagnosed dementia and cognition of 3040 cases were normal.Statistical analysis indicated that hs-CRP levels correlated with the risk of MCI,the higher the concentration of hs-CRP was,the higher the risk of MCI was(β=0.183,OR=1.201,95%CI=1.096-1.315,P=0.000);The correlation,between hs-CRP and each item score of MMSE in MCI patients was not distinctive(P0.05).Conclusions It is instructively significant to monitor the variations of concentration of serum hs-CRP for the selection of cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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".