Reappearance of Neurological Deficits in Pathologic Brain: Are Sedatives and Opioids Culprits? A Systematic Review
Bibliographic record
Abstract
Following a brain insult, focal neurological deficits may develop. Despite resolution of these deficits with time, the subsequent administration of sedative medications and opioids may lead to recrudescence of previous neurological deficits. Therefore, the present systematic review aims to explore the role of different sedatives and opioid analgesics at reproducing focal neurological deficits in patients with previous brain insults undergoing surgery. Our PRISMA compliant systematic review covering the literature from 1990 to 2020 showed a consistent reoccurrence of neurological deficits following administration of benzodiazepines and opioids across 12 studies. It appears that in all studies, the manifestations were transient and affected mostly middle-aged patients (45 to 67 y of age). In addition, benzodiazepines and opioid antagonism by naloxone and flumazenil reverses the unmasking of prior neurological deficits. In contrast, it is not clear based on our study whether the unmasking or worsening of neurological deficits occurs following recent injuries or an older brain insult, although for most patients it appears to be the former. Future studies are needed to elucidate the mechanisms involved in unmasking prior deficits and/or extension of prior injuries by sedative and opioid analgesics. This review will aid in developing prospective studies on individual sedative medications and their effects on unmasking neurological deficits in patients with multiple brain pathologies.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".