Predictors of <i>Clostridium difficile</i> colitis infections in hospitals
Bibliographic record
Abstract
Hospital-level predictors of high rates of 'Clostridium difficile-associated disease' (CDAD) were evaluated in over 2300 hospitals across California, Arizona, and Minnesota. American Hospital Association data were used to determine hospital characteristics associated with high rates of CDAD. Significant correlations were found between hospital rates of CDAD, common infections and other identified pathogens. Hospitals in urban areas had higher average rates of CDAD; yet, irrespective of geographic location, hospital rates of CDAD were associated with other infections. In addition, hospitals with 'high CDAD' rates had slower turnover of beds and were more likely to offer transplant services. These results reveal large differences in rates of CDAD across regions. Hospitals with high rates of CDAD have high rates of other common infections, suggesting a need for broad infection control policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".