A Critical Review: Does Thiopental Continuous Infusion Warrant Therapeutic Drug Monitoring in the Critical Care Population?
Bibliographic record
Abstract
Thiopental is a barbiturate used in traumatic brain injuries (TBIs) to reduce intracranial pressure (ICP) and to manage cerebral ischemia. As thiopental follows Michaelis-Menten kinetics, therapeutic drug monitoring (TDM) has been used in practice to improve efficacy and reduce adverse effects. However, its role is still debatable, and TDM is not widely practiced. Current evidence suggests that thiopental therapy may improve mortality and functional outcome in a subpopulation of patients with severe TBI with elevated ICP refractory to conventional medical therapy. Several analytical methods are available to quantify thiopental concentrations. This review uses a previously published 9-step decision-making algorithm to determine whether TDM of thiopental in TBI is warranted. There seems to be poor correlation between thiopental concentration and pharmacological response in terms of neurological response, ICP, electroencephalography, and drug toxicity. There is no established therapeutic range for thiopental continuous infusion due to a wide range of plasma concentrations corresponding to efficacy (25-50 mg/L) and toxicity (30-70 mg/L) and the resulting overlap between the 2. Thiopental exhibits intrapatient and interpatient variability due to age, obesity, renal and hepatic dysfunction, Michaelis-Menten kinetics, and hepatic enzyme autoinduction. Available evidence suggests that TDM of thiopental continuous infusion is not beneficial in improving efficacy or avoiding toxicity. There are however 2 possible scenarios in which TDM may provide additional information to sound clinical judgment. The first is providing patient-specific plasma target concentration to guide titration of therapy. The second scenario is differentiating between brain death and barbiturate-induced coma.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".