Discordance between perception and treatment practices associated with intensive care unit-acquired bacteriuria and funguria: A Canadian physician survey*
Bibliographic record
Abstract
OBJECTIVE: To determine physician practice and perception about the management of intensive care unit (ICU)-acquired bacteriuria and funguria. DESIGN: Cross-sectional, self-administered, Web-based survey. SETTING: All provinces within Canada. PARTICIPANTS: Staff ICU physicians who are members of either the Canadian Critical Care Trials Group or the Canadian Critical Care Society. INTERVENTIONS: Survey items were developed by four ICU clinicians, and survey sensibility was assessed by five independent intensivists. Nonrespondents received three follow-up reminders. Participants were asked questions about general perceptions and practices regarding the management of ICU-acquired bacteriuria and funguria. Clinical scenarios were used to elicit management strategies, including antimicrobial prescriptions. MEASUREMENTS AND MAIN RESULTS: Ninety of 198 physicians (45%) responded. Bacteriuria was perceived by 63% of the respondents to be a frequent but low-morbidity problem. Most intensivists (98%) did not use a protocol for management. Traditional symptoms were rarely used to interpret the significance of bacteriuria. Presence of systemic inflammatory response syndrome (93%), presence of hemodynamic changes (91%), and urinalysis (69%) were used often. Within clinical scenarios, source control via urinary catheter change was not universal, ranging from 44% to 67% in the various scenarios, even in patients presenting with septic shock. Prescription of antimicrobials was common across scenarios despite the low-morbidity perception. In an asymptomatic patient, 19% of respondents would prescribe antimicrobials. Changing the species from fungus to bacteria and the presence of systemic inflammatory response syndrome or shock increased the likelihood of antimicrobial use up to 70% to 80%. CONCLUSIONS: ICU physicians perceive bacteriuria to have low morbidity. However, management approaches vary considerably, and systemic antimicrobials are frequently prescribed. Increased clinical instability and bacterial vs. fungal organisms isolated in urine cultures increased the use of antimicrobials. The considerable variability in practice and discordance between likelihood of urinary tract infection and antimicrobial prescription, highlights the need for additional clinical trials.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".