Low Risk of Primary Clostridium difficile Infection With Tetracyclines: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
Background: The choice of antibiotics for systemic infections in patients with a high risk of Clostridium difficile infection (CDI) remains a clinical practice dilemma. Although some studies suggest that tetracyclines may be associated with a lower risk of CDI than other antibiotics, other results are conflicting. We conducted a systematic review and metaanalysis of studies that assessed the risk of CDI with tetracyclines compared to other antibiotics. Methods: We conducted a systematic search of Medline, Embase, and Web of Science from January 1978 through December 2016 to include studies that assessed the association between tetracycline use and risk of CDI. Weighted summary estimates were calculated using generalized inverse variance with a random-effects model using RevMan 5.3. Study quality was assessed using the Newcastle-Ottawa scale. Results: Six studies (4 case control, 2 cohort) with patient recruitment between 1993 and 2012 were included. Metaanalysis using a random-effects model, demonstrated that tetracyclines were associated with a decreased risk of CDI (odds ratio [OR], 0.62; 95% confidence interval [CI], 0.47-0.81; P < .001). There was significant heterogeneity, with an I2 of 53% with no publication bias. Subgroup analysis of studies that evaluated the risk of CDI with doxycycline alone also demonstrated a decreased risk of CDI (OR, 0.55; 95% CI, 0.40-0.75; P < .001). Conclusions: Metaanalyses of existing studies suggest that tetracyclines may be associated with a decreased risk of CDI compared with other antimicrobials. It may be reasonable to use tetracyclines whenever appropriate to decrease CDI associated with antibiotic use.
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.049 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".