Finding significant pathogens in blood cultures in children: Should we set the timer to 36 hours?
Bibliographic record
Abstract
Background: Knowledge of time to positivity (TTP) for blood cultures is useful to assess timing of discontinuation of empiric antimicrobials for suspected bacteremia with no focus. Methods: An audit of positive blood cultures from the Children's Hospital of Eastern Ontario (CHEO) from November 1, 2019, to October 31, 2020, was performed to determine TTP, defined as the start of incubation to a positive signal from automated incubators. Results: Three hundred seventy-six positive blood cultures were identified from 248 patients (average age: 6.27 [SD 6.24] years). Of these, 247 isolates were speciated; 90 (36.4%) were definitive/probable (DP) pathogens (median TTP 12.75 hours) and 157 (63.6%) possible/probable (PP) contaminants (median TTP 24.08 hours). At each time point, the adjusted rate of positive blood culture was significantly higher for DP pathogens compared to PP contaminants (hazard ratio [HR] 1.80 [95% CI 1.37, 2.36]) and for children ≤27 days old compared to the oldest age group (HR 1.94 [95% CI 1.19, 3.17]). By 36 hours, the proportion of positive cultures was significantly higher in the youngest age group (≤27 days) compared with the 3-11 years old age group (91.7% [95% CI 68.6%, 97.8%] versus 58.2% [95% CI 46.91%, 68.06%]). Conclusion: Across all ages, the TTP was significantly shorter for blood cultures with DP pathogens compared to those with PP contaminants (HR 1.80 [95% CI 1.37, 2.36]). In newborns, 90% of blood cultures were positive by 36 hours supporting this re-assessment time for empiric antimicrobials. TTP was longer in children ≥12 months, possibly related to other factors such as blood culture volume.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".