Integrated pharmacokinetic–pharmacodynamic modelling to evaluate antimicrobial prophylaxis in abdominal surgery
Bibliographic record
Abstract
OBJECTIVES: To use Monte Carlo simulation with an integrated pharmacokinetic-pharmacodynamic (PK-PD) model to evaluate guideline-recommended antimicrobial prophylaxis (AP) regimens with anaerobic coverage in abdominal surgery. METHODS: AP regimens were tested in simulated subjects undergoing elective abdominal surgery using relevant PK models and pathogen distributions in surgical site infections (SSIs). Predicted cumulative target attainment was the percentage of simulated subjects with free (unbound) antimicrobial plasma concentrations above the MICs for potential SSI pathogens. RESULTS: Cefazolin plus metronidazole covered SSI aerobes in 70% and the Bacteroides fragilis group in 99% of subjects, whereas cefoxitin only covered aerobes and anaerobes in 63% and 27% of cases, respectively. The broad-spectrum ceftriaxone plus metronidazole covered aerobes in 82% and anaerobes in 99% of simulations, while ertapenem covered aerobes in 88% and anaerobes in 90% of cases. Clindamycin covered the B. fragilis group in only 11% of cases. For cefazolin, 2 g doses maintained target attainment in simulated subjects from 80 to 120 kg, whereas 1 g doses were associated with lower target attainment against potential Gram-negative pathogens even in those <80 kg. For gentamicin, 3 mg/kg doses were comparable to the suggested 5 mg/kg, but superior to the traditional 1.5 mg/kg. CONCLUSIONS: This study demonstrates the use of PK-PD to inform decisions regarding AP in abdominal surgery. In this case, the findings support avoiding cefoxitin, avoiding clindamycin for anaerobic coverage, selecting 2 g doses of cefazolin even in patients <80 kg and using 3 mg/kg doses of gentamicin.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".