Antibiotics for delirium in older adults with pyuria or bacteriuria: A systematic review
Bibliographic record
Abstract
BACKGROUND: It is unclear whether antibiotics impact delirium outcomes in older adults with pyuria or bacteriuria in the absence of systemic signs of infection or genitourinary symptoms. METHODS: We registered our systematic review protocol with PROSPERO (CRD42023418091). We searched the Medline and Embase databases from inception until April 2023 for studies investigating the impact of antimicrobial treatment on the duration and severity of delirium in older adults (≥60 years) with pyuria (white blood cells detected on urinalysis or dipstick) or bacteriuria (bacteria growing on urine culture) and without systemic signs of infection (temperature > 37.9C [>100.2F] or 1.5C [2.4F] increase above baseline temperature, and/or hemodynamic instability) or genitourinary symptoms (acute dysuria or new/worsening urinary symptoms). Two reviewers independently screened search results, abstracted data, and appraised the risk of bias. Full-text randomized controlled trials (RCTs) and observational study designs were included without restriction on study language, duration, or year of publication. RESULTS: We screened 984 citations and included 4 studies comprising 652 older adults (mean age was 84.6 years and 63.5% were women). The four studies were published between 1996 and 2022, and included one RCT, two prospective observational cohort studies, and one retrospective chart review. None of the four studies demonstrated a significant effect of antibiotics on delirium outcomes, with two studies reported a worsening of outcomes among adults who received antibiotics. The three observational studies included had a moderate or serious overall risk of bias, while the one RCT had a high overall risk of bias. CONCLUSIONS: Our systematic review found no evidence that treatment with antibiotics is associated with improved delirium outcomes in older adults with pyuria or bacteriuria and without systemic signs of infection or genitourinary symptoms. Overall, the evidence was limited, largely observational, and had substantial risk of bias.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".