Effect of inpatient antibiotic treatment among older adults with delirium found with a positive urinalysis: a health record review
Bibliographic record
Abstract
BACKGROUND: Among older adults with delirium and positive urinalysis, antibiotic treatment for urinary tract infection is common practice, but unsupported by literature or guidelines. We sought to: i) determine the rate of antibiotic treatment and the proportion of asymptomatic patients (other than delirium) in this patient population, and ii) examine the effect of antibiotic treatment on delirium resolution and adverse outcomes. METHODS: A health record review was conducted at a tertiary academic centre from January to December 2020. Inclusion criteria were age ≥ 65, positive delirium screening assessment, positive urinalysis, and admission to general medical units. Outcomes included rates of antibiotic treatment, delirium on day 7 of admission, and 30-day adverse outcomes. We compared delirium and adverse outcome rates in antibiotic-treated vs. non-treated groups. We conducted subgroup analyses among asymptomatic patients. RESULTS: We included 150 patients (57% female, mean age 85.4 years). Antibiotics were given to 86%. The asymptomatic subgroup (delirium without urinary symptoms or fever) comprised 38% and antibiotic treatment rate in this subgroup was 68%. There was no significant difference in delirium rate on day 7 between antibiotic-treated vs. non-treated groups, (entire cohort RR 0.94 [0.41-2.16] and asymptomatic subgroup RR 0.69 [0.22-2.15]) or in 30-day adverse outcomes. CONCLUSIONS: Older adults with delirium and positive urinalysis in general medical inpatient units were frequently treated with antibiotics - often despite the absence of urinary or other infectious symptoms. We failed to find evidence that antibiotic treatment in this population is associated with delirium resolution on day 7 of admission.
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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 | 0.014 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".