Efficacy of therapeutic drug monitoring-based antibiotic regimen in critically ill patients: a systematic review and meta-analysis of randomized controlled trials
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Bibliographic record
Abstract
BACKGROUND: The efficacy of therapeutic drug monitoring (TDM)-based antimicrobial dosing optimization strategies on pharmacokinetics/pharmacodynamics and specific drug properties for critically ill patients is unclear. Here, we conducted a systematic review and meta-analysis of randomized controlled trials to evaluate the effectiveness of TDM-based regimen in these patients. METHODS: Articles from three databases were systematically retrieved to identify relevant randomized control studies. Version two of the Cochrane tool for assessing risk of bias in randomized trials was used to assess the risk of bias in studies included in the analysis, and quality assessment of evidence was graded using the Grading of Recommendations Assessment, Development, and Evaluation approach. Primary outcome was the 28-day mortality and secondary outcome were in-hospital mortality, clinical cure, length of stay in the intensive care unit (ICU) and target attainment at day 1 and 3. RESULTS: = 0%). In-hospital mortality (RR 0.96, 95% CI: 0.76-1.20), clinical cure (RR 1.23, 95% CI: 0.91-1.67), length of stay in the ICU (mean difference 0, 95% CI: - 2.18-2.19), and target attainment at day 1 (RR 1.14, 95% CI: 0.88-1.48) and day 3 (RR 1.35, 95% CI: 0.90-2.03) were not significantly different between the two groups, and all evidence for the secondary outcomes had a low or very low level of certainty because the included studies had serious risk of bias, variation of definition for outcomes, and small sample sizes. CONCLUSION: TDM-based regimens had no significant efficacy for clinical or pharmacological outcomes. Further studies with other achievable targets and well-defined outcomes are required. TRIAL REGISTRATION: Clinical trial registration; PROSPERO ( https://www.crd.york.ac.uk/prospero/ ), registry number: CRD 42022371959. Registered 24 November 2022.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.073 | 0.018 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it