.....The Relation between Multimorbidity in Elderly Patients with Mild Cognitive Impairment...
Bibliographic record
Abstract
ABSTRACT:Background: A common condition among the elderly is mild cognitive impairment (MCI), which is a stage in between normal aging and dementia. There is little data to support the link between MCI and the existence of certain chronic illnesses in older persons, despite the condition's high incidence. Early identification and prevention of further cognitive deterioration depend on an understanding of this link.Aim of the work: To assess multi-morbidity in elderly patients with MCI.Methods: A case–control study was carried out on 70 elderly participants aged 60 years and above. All participants underwent a comprehensive geriatric assessment, including cognitive evaluation using the Montreal Cognitive Assessment–Basic (MoCA-B) and assessment of comorbidities using the Charlson Comorbidity Index.Results: Multimorbidity, characterized by the presence of two or more chronic conditions, was identified in 80% of patients with mild cognitive impairment (MCI), with 28.6% exhibiting severe multimorbidity, defined as four or more chronic diseases. The MCI group exhibited a markedly greater prevalence of multimorbidity in comparison to cognitively normal controls.Conclusion: Several concomitant medical diseases are often prevalent in older persons with moderate cognitive impairment, including vascular and metabolic illnesses like diabetes and hypertension. The risk of dementia development may be decreased by early detection and treatment of various comorbidities.Keywords: Mild cognitive impairment, Multimorbidity, Charlson Comorbidity Index, MoCA-B, Elderly
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".