Bibliographic record
Abstract
Background: Pneumonia is a leading source of illness and mortality throughout the world. The majority of this research agrees on the possibility of a link between pneumonia and cognitive impairment or dementia. Objective: The effect of pneumonia on cognitive impairment was examined in this study. Materials and Methods: A matched cohort study was carried out using hospitalization data from Green Life Medical College and Hospital in Dhaka from January 2020 to November 2022, with diagnostic data classified according to the International Classification of Diseases, 10th Revision (ICD-10). Adults (18 years old) who had their first hospitalization for pneumonia throughout the study period were included. Patients were excluded if they a) had been registered with the practice for less than a year prior to admission (15), b) had hospital-acquired pneumonia (admission for at least a day in the 10 days preceding the index admission), or c) had pre-existing cognitive impairment or dementia. Controls were given the same index date as their matched pneumonia patients. Results: In the pneumonia group, half of the patients (50.0%) were determined to be at low risk, 14 (28.0%) were at moderate risk, and 11 (22.0%) were at high risk of pneumonia severity index. In terms of SMMSE scores, 5(10.0%) patients in the pneumonia group and 1(2.0%) in the control group had severe cognitive impairment. The difference between the two groups was statistically significant (p <0.05). Conclusion: In conclusion, persons who recover from pneumonia hospitalization had a higher likelihood of a new diagnosis of cognitive impairment than the general population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".