Meta-Analysis
Bibliographic record
Abstract
Background: Vancomycin is used to treat serious infections caused by methicillin-resistant Staphylococcus aureus (MRSA). It is unclear whether MRSA isolates with minimum inhibitory concentration (MIC) 1.5 to 2 µg/mL are successfully treated with vancomycin. Objective: Evaluate vancomycin failure rates in MRSA bacteremia with an MIC <1.5 versus ≥1.5 µg/mL, and MIC ≤1 versus ≥2 µg/mL. Methods: A literature search was conducted using MESH terms vancomycin, MRSA, bacteremia, MIC, treatment and vancomycin failure to identify human studies published in English. All studies of patients with MRSA bacteremia treated with vancomycin were included if they evaluated vancomycin failures, defined as mortality, and reported associated MICs determined by E-test. Study sample size, vancomycin failure rates, and corresponding MIC values were extracted and analyzed using RevMan 5.2.5. Results: Thirteen studies including 2955 patients met all criteria. Twelve studies including 2861 patients evaluated outcomes using an MIC cutoff of 1.5 µg/mL. A total of 413 of 1186 (34.8%) patients with an MIC <1.5 and 531 of 1675 (31.7%) patients with an MIC of ≥1.5 µg/mL experienced treatment failure (odds ratio = 0.72, 95% confidence interval = 0.49-1.04, P = .08). Six studies evaluated 728 patients using the cutoffs of ≤1 and ≥2 µg/mL. A total of 384 patients had isolates with MIC ≤1 µg/mL, 344 had an MIC ≥2 µg/mL. Therapeutic failure occurred in 87 and 102 patients, respectively (odds ratio = 0.61, 95% confidence interval = 0.34-1.10, P = .10). As heterogeneity between the studies was high, a random-effects model was used. Conclusion: Vancomycin MIC may not be an optimal sole indicator of vancomycin treatment failure in MRSA bacteremia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.062 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".