Use of medications that antagonize mediators of inflammatory responses may reduce the risk of delirium in older adults: a nested case–control study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to explore whether the use of medications that antagonize mediators of inflammatory responses reduces the risk of delirium in older adults. METHODS: A nested case-control study was conducted using data from a prospective study of delirium in older long-term care residents from 7 long-term care facilities in Montreal and Quebec City, Canada. The Confusion Assessment Method was used to diagnose incident delirium. The use of medications that antagonize mediators of inflammatory responses was determined by examining facility pharmacy databases and coding medications received daily by each resident. Risk sets were built using incidence density sampling: each risk set consisted of a case with incident delirium and all controls without incident delirium at the same date and facility. Conditional logistic regression was used to assess the association of exposure to inflammation antagonist medications with the incidence of delirium. RESULTS: Of 254 residents, 95 developed incident delirium during 24 weeks (cases); each case was matched with up to 35 controls. Unadjusted and adjusted odds ratios (95% CI) of delirium for residents exposed to at least one inflammation antagonist medication were 0.53 (0.34, 0.81) and 0.60 (0.38, 0.92), respectively. Estimates of the risk of incident delirium associated with specific medications and medication classes were mostly protective but not statistically significant. CONCLUSION: The use of medications that antagonize mediators of inflammatory responses may reduce the risk of delirium in older adults. Despite study limitations, the findings merit further investigation using larger patient samples, more precise measures of exposure and better control of potential confounding variables. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".