Thiopental‐Induced Neutropenia in Two Patients with Severe Head Trauma
Bibliographic record
Abstract
Thiopental has been used for decades in the treatment of refractory intracranial hypertension in patients with traumatic and nontraumatic head injuries. Commonly reported adverse effects include hypotension, hypokalemia, respiratory complications, and hepatic dysfunction. Neutropenia has rarely been reported as an adverse effect of thiopental. We witnessed probable thiopental-induced neutropenia in two patients with traumatic brain injuries who developed increased intracranial hypertension that was refractory to standard therapy. Based on a MEDLINE search of published case reports and literature, we propose two mechanisms by which thiopental-related neutropenia might be explained. The first is inhibition of inflammatory mediator nuclear factor-kappa B (NF-kappa B), leading to granulocyte apoptosis. The second mechanism involves inhibition of calcineurin. Although the precise link between these two mechanisms has not been elucidated, calcineurin is known to regulate NF-kappa B activity. Development of neutropenia does not appear to be correlated with time but may correlate with plasma concentrations of thiopental. The optimum management of drug-induced neutropenia is unclear. The decision to discontinue thiopental in patients who develop neutropenia should be made by weighing the risks versus benefits. Broad-spectrum antibiotics may be required in the presence of fever. The role of hematopoietic growth factors such as granulocyte colony-stimulating factor is not yet defined. Given the adverse infectious consequences of neutropenia, it is essential to closely monitor neutrophil counts in patients receiving thiopental.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".