The association of antibiotic pharmacodynamic indices with survival in human septic shock
Bibliographic record
Abstract
Septic shock and sepsis associated multiple organ failure are a major cause of morbidity and mortality in intensive care units (ICUs) globally. The treatment of patients with septic shock remains one of the major challenges to ICU clinicians. In this research, 342 bacteremic patients with septic shock were determined to have been treated with a β-lactam monotherapy. Objective: To show that key pharmacokinetic indices for a wide variety of β–lactams are associated with outcome in human septic shock. Methods: β-lactam pharmacodynamic (PD) indices including time above MIC and four times above MIC (ƒT>MIC, ƒT>4X MIC). Logistic regression analysis of 1st 24 hour time above MIC (p=.0005) and 1st 24 hour time above 4X MIC (p=.0003) were strongly associated with the improved survival of septic shock. Neither the 1st 24 hour Cpeak/MIC (p=.0762) or 1st 24 hour AUC/MIC (p=.0872) achieved significance in relation to outcome in logistic regression analysis.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".