Evaluating the CT Diagnosis of <i>Clostridium difficile</i> Colitis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to further characterize the CT findings of Clostridium difficile colitis and to provide for the first time a diagnostic sensitivity, specificity, positive predictive value, and negative predictive value to help clinicians decide whether antibiotic treatment is warranted on the basis of CT findings while awaiting stool test results (which may take as long as 48 hr). MATERIALS AND METHODS: A retrospective review covering a 4-year period was performed of the charts and CT scans of 54 symptomatic patients with stool test results positive for C. difficile and of a control group of 56 patients with antibiotic-associated diarrhea with stool test results negative for C. difficile. RESULTS: At our institution, C. difficile colitis was explicitly diagnosed at CT in these patients with a sensitivity of 52%, specificity of 93%, positive predictive value of 88%, and negative predictive value of 67%. The sensitivity can be raised to 70% with no change in specificity with more rigid adherence to diagnostic criteria of colon wall thickening of greater than 4 mm combined with any one or more findings of pericolonic stranding, colon wall nodularity, the "accordion" sign, or otherwise unexplained ascites. CONCLUSION: Although routine CT screening of antibiotic-associated diarrhea is not advocated, the 88% positive predictive value of a diagnosis of C. difficile colitis in those who are scanned may merit consideration of treatment by clinicians on the basis of the CT results alone.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".