False Negative Results in Clostridium difficile Testing
Bibliographic record
Abstract
BACKGROUND: Accurate diagnosis of Clostridium difficile infection (CDI) is paramount for patient management. The wrong diagnosis places patients at risk, delays treatment, and/ or contributes to transmission of infection in the healthcare setting. Although amplification of the toxin B gene by polymerase chain reaction (PCR) is a sensitive method for detecting toxigenic C. difficile, false negative results still occur and could impact the diagnosis and treatment of this infection. METHODS: This study investigated 48 patients that tested negative for toxigenic C. difficile via GeneXpert C. difficile epi test, while simultaneously testing positive for toxigenic C. difficile via stool culture. Fifty discrepant samples were collected over a 15-month period and all C. difficile isolates were characterized by ribotype. Patient charts were reviewed to assess whether discrepant results impacted the treatment course or clinical outcome of affected patients. RESULTS: Fifty samples of a total of 2308 samples tested in an acute healthcare facility over a 15-month period had negative PCR and positive stool culture for toxigenic C. difficile. C. difficile isolated from the discrepant samples resulted in diverse ribotyping patterns suggesting they were derived from different strains. The samples belonged to patients who were distributed evenly between age groups and wards in the hospital. In the majority of cases, the false negative C. difficile test results did not seem to impact the clinical outcome in these patients. CONCLUSIONS: The PCR limit of detection may impact the results of molecular methods for C. difficile detection. Both clinical and analytical sensitivity of C. difficile tests should be considered when deciding which diagnostic assay to use, and clinical correlates should be examined carefully before excluding CDI as a cause of disease.
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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 | 0.019 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".