Pharmacotherapeutic advances in fibromyalgia: what’s new on the horizon?
Bibliographic record
Abstract
INTRODUCTION: This review delves into Fibromyalgia Syndrome (FMS), a chronic pain condition demanding thorough understanding for precise diagnosis and treatment. Yet, a definitive pharmacological solution for FMS remains elusive. AREAS COVERED: In this article, we systematically analyze various pharmacotherapeutic prospects for FMS treatment, organized into sections based on the stage of drug development and approval. We begin with an overview of FDA-approved drugs, discussing their efficacy in FMS treatment. Next, we delve into other medications currently used for FMS but still undergoing further study, including opioids and muscle relaxants. Further, we evaluate the evidence behind medications that are currently under study, such as cannabinoids and naltrexone. Lastly, we explore new drugs that are in phase II trials. Our research involved a thorough search on PUBMED, Google Scholar, and clinicaltrials.gov. We also discuss the action mechanisms of these drugs and their potential use in specific patient groups. EXPERT OPINION: A focus on symptom-driven, combination therapy is crucial in managing FMS. There is also a need for ongoing research into drugs that target neuroinflammation, immunomodulation, and the endocannabinoid system. Bridging the gap between benchside research and clinical application is challenging, but it holds potential for more targeted and effective treatment strategies.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".