Reducing Bloodstream Infections in Pediatric Rehabilitation Patients Receiving Parenteral Nutrition
Bibliographic record
Abstract
OBJECTIVE: To report our quality improvement efforts to reduce total parenteral nutrition (TPN)-associated bloodstream infections, and the results of those efforts, during the period including the first quarter of 2004 through the third quarter of 2010. METHODS: A variant on failure modes and effect analysis and existing guidelines were used to develop and modify interventions. Effectiveness of the interventions was assessed by using a graphical depiction of interrupted time-series data on TPN-associated infections per 1000 TPN-days, aggregated across quarters within intervention periods. RESULTS: Although initial interventions yielded limited reductions in infection rates, it was not until the implementation of a multifaceted "maintenance intervention bundle" that rates strongly responded. After this key intervention revision, the TPN-associated infection rate decreased between implementation in the first quarter of 2008 from 26.1 to 4.8 per 1000 TPN-days during the 8 quarters aggregated comprising the first quarter of 2008 through the fourth quarter of 2009. The final addition of an alcohol-swab cap resulted in a reduction of rates to 0 for the first three-quarters of 2010. CONCLUSIONS: Our evidence suggests that iterative design/redesign of interventions using failure modes and effect analysis has directly reduced TPN-associated bloodstream infections.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".