Impact of the Type of Diagnostic Assay on Clostridium difficile Infection and Complication Rates in a Mandatory Reporting Program
Bibliographic record
Abstract
BACKGROUND: Most Clostridium difficile infection (CDI) surveillance programs neither specify the diagnostic method to be used nor stratify rates accordingly. We assessed the difference in healthcare-associated CDI (HA-CDI) incidence and complication rates obtained by 2 validated diagnostic methods. METHODS: This was a prospective cohort study of patients for whom a C. difficile test was ordered between 1 August 2010 and 31 July 2011. All specimens were tested in parallel by a commercial polymerase chain reaction (PCR) assay targeting toxin B gene tcdB, and a 3-step algorithm detecting glutamate dehydrogenase and toxins A and B by enzyme immunoassay and cell culture cytotoxicity assay (EIA/CCA). CDI incidence rate ratios were calculated using univariate Poisson regression. RESULTS: A total of 1321 stool samples were tested during a period totaling 95 750 patient-days. Eighty-five HA-CDI cases were detected by PCR and 56 cases by EIA/CCA (P = .01). The overall incidence rate was 8.9 per 10 000 patient-days (95% confidence interval [CI], 7.1-10.9) by PCR and 5.8 per 10 000 patient-days (95% CI, 4.4-7.4) by EIA/CCA (P = .01). The incidence rate ratio comparing PCR and EIA/CCA was 1.52 (95% CI, 1.08-2.13; P = .015). Overall complication rate was 27% (23/85) when CDI was diagnosed by PCR and 39% (22/56) by EIA/CCA (P = .16). Cases detected by PCR only were less likely to develop a complication of CDI compared with cases detected by both PCR and EIA/CCA (3% vs 39%, respectively; P < .001). CONCLUSIONS: Performing PCR instead of EIA/CCA is associated with a >50% increase in the CDI incidence rate. Standardization of diagnostic methods may be indicated to improve interhospital comparison.
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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 | 0.023 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".