Rates of 1-year cognitive impairment in older adults who developed delirium due to a systemic infection
Bibliographic record
Abstract
Introduction Delirium affects a significant proportion of hospitalized older patients with acute infections. There is growing evidence that delirium accelerates the cognitive decline at long term. Objectives We aimed to determine if delirium during hospitalization was independently associated with cognitive deterioration at one-year. Methods From a total of 22 patients (12 C, 4 Dem, 2 D, and 4 DD) delirium (D and DD groups) was associated with a worse score in MOCA of 3-points (p<.02) and 2.5-points (p<.03), respectively, at one year, follow up. Dementia patients without delirium had a decrease of 2-point (p=.04) while cognitively healthy patients had a decrease in 1.08 points (p=.05) (Graph1). MOCA and NPI scores during hospitalization correlated significantly with cognitive decline in the four groups (r=.658, p<.01 and r=.439, p=.02, respectively.) Results From a total of 22 patients (12 C, 4 Dem, 2 D and 4 DD) delirium (D and DD groups) was associated with a worse score in MOCA of 3-points (p<.02) and 2.5-points (p<.03), respectively, at one year follow up. Dementia patients without delirium had a of 2-point (p=.04) while cognitively healthy patients had a decrease in 1.08 points (p=.05) (Graph1). MOCA and NPI scores during hospitalization correlated significantly with cognitive decline in the four groups (r=.658, p<.01 and r=.439, p=.02, respectively.) Conclusions Individuals developing delirium while recovering from infection have higher rates of cognitive decline after one year, but the cognitive decline is also present to a lower extent for individuals with infections that did not develop delirium. Disclosure No significant relationships.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".