388. Does This Patient Have <i>C. difficile</i> Infection? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background The clinical features of Clostridioides difficile infection overlap with many conditions. Accurate and early diagnosis of C. difficile is essential for reducing morbidity and mortality. We performed a systematic review to evaluate the diagnostic utility of clinical findings associated with C. difficile. Methods We included all studies that reported clinical features of C. difficile, a valid reference standard test for confirming diagnosis of C. difficile, and a comparison among patients with a positive and negative test result. The MEDLINE, EMBASE, CINAHL, and Cochrane databases were searched up to September 2021. Meta-analyses using univariate and bivariate methods were used to determine estimates of sensitivity, specificity, and likelihood ratios (LRs). Results A total of 11,231 articles were screened and 46 were included for final analysis, enabling evaluation of 67 features for their diagnostic utility for C. difficile (10 clinical examination findings, 4 laboratory tests, 10 radiographic findings, prior exposure to 14 antibiotic types, and 29 clinical risk factors). Of the ten features identified on clinical examination, none were associated with increased likelihood of C. difficile infection. Features that increased likelihood of C. difficileinfection were stool leukocytes (LR 5.31, 95% CI 3.29-8.56), hospital admission in prior three months (LR 2.39, 95% CI 1.68-3.30), leukocytosis (LR 1.50, 95% CI 1.24-1.75), and low serum albumin (LR 1.43, 95% CI 1.06-1.97). Some clinical co-morbidities increased likelihood of C. difficile including congestive heart failure (LR 3.01, 95% CI 2.26-3.80) and end-stage renal disease (LR 3.85, 95% CI 1.73-7.57). Several radiographic findings also strongly increased the likelihood of C. difficile infection like pericolonic stranding (LR 10.72, 95% CI 9.59-11.84) and ascites (LR 2.91, 95% CI 1.76-4.80). Conclusion There is limited utility of bedside clinical examination alone in detecting or ruling out C. difficile infection. Accurate diagnosis of C. difficile infection requires a combination of thoughtful clinical assessment and interpretation of test results. However, microbiologic testing is needed for confirmation in all suspected cases. Disclosures 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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".