Time to Positivity of Blood Cultures in Infants 0 to 90 Days Old Presenting to the Emergency Department: Is 36 Hours Enough?
Bibliographic record
Abstract
BACKGROUND: Continuous monitoring blood culture systems (CMBCS) now allow for more rapid detection of microbial growth. We aimed to determine whether a 36-hour period was sufficient to detect all blood cultures positive for pathogenic bacteria in infants 0 to 90 days old undergoing a septic workup in the emergency department of a tertiary care pediatric center. METHODS: We performed a retrospective study of all positive blood cultures collected in these infants over a 5-year time period (from March 13, 2008 to July 29, 2013). Bottles were incubated in a CMBCS. The time to positivity (TTP) was calculated from time of blood culture registration into the laboratory system to time of Gram stain. Medical charts were reviewed for relevant clinical information. Cultures were classified as pathogenic or contaminant using microorganism type and clinical presentation. RESULTS: Three thousand five hundred fifty-nine blood cultures were collected. Of these, 98 (2.8%) were positive. Fifty-two (53.1%) were deemed pathogenic and 46 (46.9%) were deemed contaminant, for a true prevalence of bacteremia of 1.5%. At 24, 36, 48, and 50 hours, 87.8% (86 of 98), 96.9% (95 of 98), 99% (97 of 98), and 100% (98 of 98) of all cultures were positive. Considering only pathogenic organisms, 96.1% (50 of 52) and 100% (52 of 52) were positive at 24 and 36 hours. Mean TTP for pathogens and contaminants was 14.40 and 23.18 hours, respectively (P < .001). CONCLUSIONS: An incubation period of 36 hours is sufficient to detect 100% of blood cultures positive for a pathogenic organism in our population.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".