Presence of urinary symptoms in bacteremic urinary tract infection: a retrospective cohort study of Escherichia coli bacteremia
Bibliographic record
Abstract
Abstract Background It is important to understand clinical features of bacteremic urinary tract infection (bUTI), because bUTI is a serious infection that requires prompt diagnosis and antibiotic therapy. Escherichia coli is the most common and important uropathogen. The objective of our study was to characterize the clinical presentation of E coli bUTI. Methods Retrospective cohort study of consecutive adult patients admitted for community acquired E. coli bacteremia from January 1, 2015 to December 31, 2016 was conducted at 4 acute care academic and community hospitals in Toronto, Ontario, Canada. Logistic regression models were developed to identify E coli bUTI cases without urinary symptoms. Results Of 462 patients with E. coli bacteremia, 284 (61.5%) patients had a urinary source. Of these 284 patients, 161 (56.7%) had urinary symptoms. In a multivariable model, bUTI without urinary symptoms were associated with older age (age =85 years had OR of 2.95 95% CI 1.44–6.18 p = 0.0036) and delirium (OR of 2.12 95% CI 1.13–4.03 p = 0.0207). Sepsis by SIRS criteria was present in 274 (96.5%) of all bUTI cases and 119 (96.8%) of bUTI cases without urinary symptoms. Conclusion The majority of patients with E. coli bacteremia had a urinary source. A significant proportion of bUTI cases had no urinary symptoms elicited on history. Elderly and delirious patients were more likely to have bUTI without urinary symptoms. In elderly and delirious patients with sepsis by SIRS criteria but without a clear infectious source, clinicians should suspect, investigate, and treat for bUTI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".