Right Dose, Right Now: Customized Drug Dosing in the Critically Ill
Bibliographic record
Abstract
Drugs are key weapons that clinicians have to battle against the profound pathologies encountered in critically ill patients. Antibiotics in particular are commonly used and can improve patient outcomes dramatically. Despite this, there are strong opportunities for further reducing the persisting poor outcomes for infected critically ill patients. However, taking these next steps for improving patient care requires a new approach to antibiotic therapy. Giving the right dose is highly likely to increase the probability of clinical cure from infection and suppress the emergence of resistant pathogens. Furthermore, in some patients with higher levels of sickness severity, reduced mortality from an optimized approach to antibiotic use could also occur. To enable optimized dosing, the use of customized dosing regimens through either evidence-based dosing nomograms or preferably through the use of dosing software supplemented by therapeutic drug monitoring data should be embedded into daily practice. These customized dosing regimens should also be given as soon as practicable as reduced time to initiation of therapy has been shown to improve patient survival, particularly in the presence of septic shock. However, robust data supporting these logical approaches to therapy, which may deliver the next step change improvement for treatment of infections in critically ill patients, are lacking. Large prospective studies of patient survival and health system costs are now required to determine the value of customized antibiotic dosing, that is, giving the right dose at the right time.
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How this classification was reachedexpand
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".