A Multifaceted Approach to the Prevention of Clostridioides (Clostridium) Difficile
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: The purpose of this project was to design and implement a sustainable program to reduce hospital-acquired cases of Clostridioides difficile. DESCRIPTION OF THE PROJECT: Experiencing higher rates in a large, academic medical center, hospital leaders were assembled. The overall facility rate was 6.9% in 2014 with a first quarter rate of 8.4% in 2015. Individual unit rates were as high as 19.8%. A team of key stakeholders was assembled to plan, execute, and reevaluate targeted solutions. Strategies implemented were an innovative, automated screening tool, an evidence-based prevention bundle; and staff education. OUTCOMES: A facility-wide C difficile prevention program was implemented with a sustained decrease in rates observed from 8.4% in the first quarter of 2015 to 6.0% in the fourth quarter of 2017. The standardized infection ratio ranged from 0.541 to 0.889, consistently below the national mean. CONCLUSION: Clostridioides difficile is a leading cause of hospital-associated diarrhea and a tremendous burden on healthcare systems increasing morbidity, mortality, and financial strain. A multidisciplinary, multifaceted approach was critical to ensure early detection, reduce risk of transmission, and decrease overall rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".