Efficacy and safety of co-trimoxazole in device-related bone and joint infections: a CRIOGO multicentre case–control study
Bibliographic record
Abstract
OBJECTIVES: Co-trimoxazole could be an alternative antibiotic to treat device-related bone and joint infection (BJI) but there are few published data about its efficacy and safety in this complex scenario to treat infection. The objective was to compare the outcome of patients with device-related BJI treated with an antibiotic regimen including co-trimoxazole versus a regimen without co-trimoxazole. METHODS: This multicentre case-control study included consecutive adult patients diagnosed with device-related BJI. Each patient receiving co-trimoxazole was included in the co-trimoxazole group and was matched with two control patients, with stratification on microbial aetiology and age. The primary outcome was composite and defined by death or treatment failure during the follow-up. RESULTS: In this study, 150 patients were included, 50 in the co-trimoxazole group and 100 in the control group. The rate of reaching the primary endpoint was 18% in the co-trimoxazole group (9/50 cases) versus 21% in the control group (21/100) (P = 0.66). Co-trimoxazole use was not associated with an unfavourable outcome in the multivariate analysis (adjusted OR 0.8, 95% CI 0.31-2.06, P = 0.64). Although no significant difference was observed in premature discontinuation of treatment due to an adverse event between both groups (14 versus 12%, P = 0.73), treatment-related adverse events were significantly more frequently reported in patients of the co-trimoxazole group than the control group [34% (17/50) versus 18% (18/100), P = 0.03]. CONCLUSIONS: Co-trimoxazole appears to be an effective alternative for the treatment of BJI, even when it occurs on a device, but the safety profile requires close monitoring of adverse effects.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".