Cognitive profile in patients with bronchial asthma and chronic obstructive pulmonary disease (COPD)
Bibliographic record
Abstract
Objective To evaluate the cognitive status in patients with bronchial asthma and chronic obstructive pulmonary disease (COPD). Methods 40 patients with bronchial asthma, 40 patients with COPD and 20 healthy subjects (control) were included in the study. Comparison was done between the three groups in both Montreal Cognitive Assessment (MoCA) test and P300 latency. Also, correlation between these scores and patient characteristic parameters were evaluated. Results There was a significant prolongation in P300 latency (P < 0.04) and reduced MoCA scores in COPD group compared to asthma group (P < 0.002). 34/40 COPD patients had prolongation of P300 latency and reduced MoCA scores. However, 20/40 asthma patients had prolongation of P300 latency and 24/40 asthma patients had reduced MoCA scores. P300 latency correlated significantly with age (r = 0.423, P < 0.007), duration of disease(r = 0.622, P < 0.0001) PaO2 (r = −0.490, P < 0.001), SaO2 (r = −0.496, P < 0.003) and degree of the disease (FEV1/FVC) (r = −0.353, P < 0.026) in COPD group. MoCA score was significantly correlated with WBC (r = 0.45) in COPD group and with BMI (r = 0.236, P < 0.05) in asthma group. There was no correlation between P300 latency and patients’ characteristics in asthma group (P > 0.05). Conclusions COPD significantly decreased the cognitive status compared to bronchial asthma. Longer latency of P300 appears to be an expected sequel of COPD. MoCA abnormalities were comparable to P300 abnormalities in COPD and asthma 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".