493. Development of a Simple <i>Clostridium difficile</i> Infection Clinical Risk Prediction Tool for Medical Inpatients
Bibliographic record
Abstract
Prevention of Clostridium difficile infection (CDI) remains a significant healthcare challenge. Risk prediction tools can potentially identify high-risk patients and allow for early prophylactic interventions. Various tools have been studied but none have been widely adopted. Our objective was to develop a simple risk prediction tool to identify medicine inpatients at high risk for developing primary CDI. We conducted a retrospective, single-centre case–control study including patients admitted to the internal medicine service at our institution with a positive C. difficile polymerase chain reaction assay in loose stool. Controls were randomly selected from the same population. Risk factors for CDI were identified using univariate and multivariate logistic regression analyses. A model was created using variables that minimized Akaike Information Criterion and yielded higher area under the curve values. A total of 314 patients were included (157 with CDI and 157 controls). Variables included in the final 5-point, 3-variable risk prediction tool were age, modified Horn’s index and antibiotic use within 3 months. The tool demonstrated good discrimination with a C statistic of 0.79 and model optimism of 0.04 based on a bootstrap sample of 2,000 replicates. Our simple 3-variable risk prediction tool based on age, disease severity and recent antibiotic use facilitates rapid bedside assessment by clinicians to identify medicine patients at high risk of CDI on admission. Further research is needed to determine whether this tool can reduce primary CDI incidence and healthcare costs. Five-Point CDI Clinical Risk Tool and Predicted Risk. Receiver operating characteristic (ROC) curve for CDI risk prediction model CDI risk prediction model calibration plot. Clostridium difficile infection risk prediction model calibration plot showing agreement between observed and predicted risks. Dashed line shows perfect agreement; lines above the dashed line indicate the predicted risks are lower than the actual risk. All authors: No reported disclosures.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".