The future of translational research on alcohol use disorder
Bibliographic record
Abstract
In March 2019, a scientific meeting was held at the University of California, Los Angeles (UCLA) Luskin Center to discuss approaches to expedite the translation of neurobiological insights to advances in the treatment of alcohol use disorder (AUD). A guiding theme that emerged was that while translational research in AUD is clearly a challenge, it is also a field ripe with opportunities. Herein, we seek to summarize and disseminate the recommendations for the future of translational AUD research using four sections. First, we briefly review the current landscape of AUD treatment including the available evidence-based treatments and their uptake in clinical settings. Second, we discuss AUD treatment development efforts from a translational science viewpoint. We review current hurdles to treatment development as well as opportunities for mechanism-informed treatment. Third, we consider models of translational science and public health impact. Together, these critical insights serve as the bases for a series of recommendations and future directions. Towards the goal of improving clinical care and population health for AUD, scientists are tasked with bolstering the clinical applicability of their research findings so as to expedite the translation of knowledge into patient care.
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 | 0.172 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".