Introduction to the Special Issue on: Benzodiazepine Side-Effects: From the Bench to the Clinic
Bibliographic record
Abstract
This article introduces a special issue of Current Pharmaceutical Design focusing on the various side effects of benzodiazepine medications. We argue that an increased awareness of the risk of dependence, withdrawal symptoms upon discontinuation, and cognitive side effects of the benzodiazepines has likely contributed to the decline in their prescription rate over the last two decades, as has increased availability of alternative pharmacologic and non-pharmacologic treatments for anxiety and insomnia. The present special issue consists of series of five papers covering current issues in the area of benzodiazepine side effects. These reviews cover a wide range of topics pertaining to adverse, unintended consequences of this class of pharmacologic agents including their potential for tolerance and withdrawal, their profile of associated cognitive impairments, as well as current understanding of means for minimizing these unintended effects. The reviews also cover a variety of methodologies and disciplines from laboratory-based research findings with animals, to laboratory-based studies with healthy human volunteers, to findings obtained in the clinic with anxious patients. All reviews are timely contributions, covering highly relevant topics for consideration of benzodiazepine side effects at present. The papers presented herein should serve to stimulate future research that may ultimately help improve the quality of life of those patients living with debilitating anxiety-related conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".