Trends in Outpatient Bacteremia in British Columbia, Canada between 2010 and 2015
Bibliographic record
Abstract
Understanding the epidemiology of etiologic organisms that cause bacteremia in the community setting is important for treatment and prevention strategies. In British Columbia, Lifelabs Medical Laboratories receives specimens submitted for blood culture from 128 community collection sites distributed across the province. We explored the trends in outpatient blood cultures in British Columbia between 2010 and 2015. Blood cultures drawn at community based Lifelabs collection sites between 2010–2015 were included in the study. Cultures and identification were performed according to routine laboratory methods. Basic demographic information including age, gender, and organism cultured were collected. Antibiotic-resistant organisms included methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus faecalis or faecium (VRE), ciprofloxacin-resistant Salmonella sp., extended spectrum β lactamase (ESBL) positive Enterobacteriaceae and carbapenem-resistant Enterobacteriaceae (CRE). Between 2010- 2015, a total of 16,619 sets of blood cultures were drawn, for an average sampling rate of 60.56 per 100,000 population. Of the total of 16,619 sets of blood cultures, 342 sets (2.05%) were considered to be significant isolates. The 66 sets of contaminants (0.04%) included predominantly coagulase negative staphylococci (CNS). The eight most common organisms constituted 85.3% of the total positive isolates. Among these organisms, there was no significant change in proportion between 2010 and 2015. Both Salmonella sp. and Streptococcus pneumoniae showed a proportional decrease with age (P ≤ 0.008), while Klebsiella pneumoniae showed a proportional increase with age (P ≤ 0.001). No VRE or CRE were noted, while 30.4% of Salmonella sp. were ciprofloxacin resistant, 17.4% of Enterobacteriaceae were ESBL positive, and 6.45% of Staphylococcus aureus were MRSA. When analyzed over a 6-year period, there were no significant changes in the proportion of identified organisms. The proportion of antibiotic-resistant organism in the community is low. R. Reyes, Lifelabs Medical Laboratories: Employee, Salary
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".