Clinical significance of cefazolin inoculum effect in serious MSSA infections: a systematic review
Bibliographic record
Abstract
Background: The cefazolin inoculum effect (CzIE) is a phenomenon whereby some MSSA isolates demonstrate resistance to cefazolin when a high bacterial inoculum is used for susceptibility testing. The clinical significance of this phenotypic phenomenon remains unclear. We conducted a systematic review to answer the following question: In patients with serious MSSA infection treated with cefazolin, does infection due to CzIE-positive MSSA isolates result in worse clinical outcomes than infection due to CzIE-negative MSSA isolates? Methods: Ovid MEDLINE, Embase, Cochrane CENTRAL, medRxiv and bioRxiv were searched from inception until 12 April 2023. Studies were included if they tested for CzIE in clinical isolates from MSSA infections in humans. Two independent reviewers extracted data and conducted risk-of-bias assessment. Main outcomes were treatment failure and mortality. Pooling of study estimates was not performed given the heterogeneity of patient populations and outcome definitions. Results: Twenty-three observational studies were included. CzIE presence amidst MSSA isolates ranged from 0% to 55%. There was no statistically significant mortality difference in two studies that compared MSSA infections with and without CzIE, with ORs ranging from 0.72 to 19.78. Of four studies comparing treatment failure, ORs ranged from 0.26 to 13.00. One study showed a significantly higher treatment failure for the CzIE group, but it did not adjust for potential confounders. Conclusions: The evidence on CzIE is limited by small observational studies. In these studies, CzIE did not predict higher mortality in MSSA infections treated with cefazolin. Our findings do not support CzIE testing in clinical practice currently.
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".