Can leukotriene receptor inhibitors preserve cognitive functions in asthma patients?
Bibliographic record
Abstract
Objective. Recent research suggests that neuroinflammation may play a crucial role in age-related cognitive dysfunction. Studies have investigated the impact of leukotriene receptor (LTR) antagonist montelukast, a medication commonly used to treat allergies and asthma, on the risk of developing dementia. The aim of this study was to compare cognitive functions in asthmatic patients who used montelukast, those who did not use it, and healthy controls. Methods. In this study 50 asthmatic patients (age 67.34±6.44; 20 montelukast users and 30 non-users) were matched with 50 healthy controls (age 68.08±5.95). Each participant was assessed using the Mini Mental State Examination (MMSE) and the Montreal Cognitive Test (MoCA). Memory coefficients (verbal, visual, general, delayed memory, and attention and concentration) were measured by Wechsler Memory Scale-Revised (WMS-R). Results. Orientation, as MoCA subdomain, was worse in patients with asthma compared to the control group (p=0.048). Asthmatic patients treated with montelukast had a significantly higher MoCA score (p=0.033), dominantly due to delayed recall, (p=0.044) than patients without montelukast treatment. People with asthma on montelukast therapy showed statistically significantly better results on the MoCA test (p=0.037) compared to the control group (dominantly due to attention (p=0.032) and delayed recall (p=0.021)). A multiple linear logistic regression model showed statistical significance (p=0.025). The use of montelukast had significant impact on the results of the MOCA test (p=0.012). Conclusion. Montelukast group had better results on MoCA (dominantly due to delayed recall) compared to montelukast non-users. Studies with a larger number of participants are needed to confirm the effect of montelukast on the cognitive status of the 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".