Impact of Matrix-Assisted Laser Desorption and Ionization Time-of-Flight and Antimicrobial Stewardship Intervention on Treatment of Bloodstream Infections in Hospitalized Children
Bibliographic record
Abstract
BACKGROUND.: Early definitive identification of infectious pathogens coupled with antimicrobial stewardship interventions allow for targeted and timely administration of antimicrobials. We investigated the combined impact of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and an antimicrobial stewardship program (ASP) in pediatric patients with blood stream infections (BSIs). METHODS.: This is a single-center study comparing a control group of patients from October 2009 to July 2010 with BSIs to a cohort of patients postimplementation of MALDI-TOF and an ASP, from October 2013 to July 2014. Primary outcome was time to optimal therapy. Secondary outcomes included time to effective therapy, 30-day all-cause mortality, 30-day readmission rate, hospital length of stay, and intensive care admission. RESULTS.: One hundred episodes of BSIs were identified in the preintervention period, and 121 episodes were identified in the postintervention period. Time from blood culture collection to organism identification was significantly reduced in the prospective cohort compared with historical controls (18.8 vs 43.7 hours, respectively). A total of 73 ASP interventions were made on the treatment of BSIs in the postintervention period. Combined use of MALDI-TOF and ASP significantly reduced time to optimal therapy (77.0 to 54.2 hours, P < .001). In the subgroup analysis of Gram-negative bacteremia, time to effective and optimal therapy were significantly reduced (2.0 vs 0.7 hours and 146.8 vs 48.0 hours, respectively). There were no significant differences in clinical outcomes. CONCLUSIONS.: The combined use of MALDI-TOF and ASP allows early optimization of antimicrobial therapy in pediatric inpatients with BSIs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".