Pharmacotherapy variability and precision medicine in neurocritical care
Bibliographic record
Abstract
Pharmacotherapy variability is defined as the variability in drug response among and within individuals that is attributed to the inter and intra-individual differences in the action and disposition of drugs. Neurological and medical complications in neurocritical care contribute significantly to the overall disease prognosis. Pharmacological management plays a key role in managing many of those complications such as cerebral vasospasm, delayed cerebral ischemia, hyponatremia, infections, and seizures. However, pathophysiologic changes secondary to neurological and critical illnesses make the medical management of these patients challenging, contributing to pharmacotherapy variability. Interindividual differences in disease pathophysiology, altered organ function, systemic inflammation, hemodynamic instability, and common interventions employed in intensive care settings could alter the pharmacokinetics and pharmacodynamics of medications. The use of potentially ineffective treatments and suboptimal dosing of medications to manage patients can lead to poor outcomes as the understanding of the effect of neurological injury on the action and disposition of drugs is limited. This narrative review highlights the factors contributing to pharmacotherapy variability in neurocritical care, equipping clinicians with critical insights to refine patient management strategies. In conclusion, pharmacotherapy variability within neurocritical care introduces additional layers of complexity that may significantly contribute to therapy failure, adverse drug reactions, and setbacks in drug development. Understanding these variations is essential for identifying subpopulations that may derive the greatest benefit from specific therapies, representing a critical step toward achieving precision medicine in neurocritical care, ensuring the administration of the appropriate medication to the right patient at the correct dosage regimen.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".