Evaluation of a <i>Clostridium difficile</i> infection management policy with clinical pharmacy and medical microbiology involvement at a major Canadian teaching hospital
Bibliographic record
Abstract
WHAT IS KNOWN AND OBJECTIVE: Clostridium difficile infection (CDI) represents a spectrum of disease and is a significant concern for healthcare institutions. Our study objective was to assess whether implementation of a regional CDI management policy with Clinical Pharmacy and Medical Microbiology and Infection Control involvement would lead to an improvement in concordance in prescribing practices to an evidence-based CDI disease severity assessment and pharmacological treatment algorithm. METHODS: Conducted at a tertiary care teaching hospital, this two-phase quality assurance study consisted of a baseline retrospective healthcare record review of patients with CDI prior to the implementation of a regional CDI management policy followed by a prospective evaluation post-implementation. RESULTS AND DISCUSSION: One hundred and forty-one CDI episodes in the pre-implementation group were compared to 283 episodes post-implementation. Overall treatment concordance to the CDI treatment algorithm was achieved in 48 of 141 cases (34%) pre-implementation compared with 136 of 283 cases (48·1%) post-implementation (P = 0·01). The median time to treatment with vancomycin was reduced from five days to one day (P < 0·01), with median length of hospital stay decreasing from 30 days to 21 days (P = 0·01) post-implementation. There was no difference in 30-day all-cause mortality. WHAT IS NEW AND CONCLUSION: A comprehensive approach with appropriate stakeholder involvement in the development of clinical pathways, education to healthcare workers and prospective audit with intervention and feedback can ensure patients diagnosed with CDI are optimally managed and prescribed the most appropriate therapy based on CDI disease severity.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".