Neuroscience-based Nomenclature (NbN) and Early Career Psychiatrists: A Cross-Sectional Study on Views, Attainment and Needs
Bibliographic record
Abstract
Introduction Anatomical Therapeutic Chemical (ATC) indication-based classification system is the World Health Organization (WHO) drug classification system and it is widely used in clinical and researh practice, however there has been questions around the scientific base of this (1, 2). Neuroscience-based Nomenclature (NbN) has been developed by representatives from 5 international organizations, with specific expertise in psychopharmacology, to address the issues around neuropsychopharmacological drug classification and improve the focus on pharmacological domains and mode of action: ECNP – European College of Neuropsychopharmacology ACNP – American College of Neuropsychopharmacology AsCNP – Asian College of Neuropsychopharmacology CINP – International College of Neuropsychopharmacology IUPHAR – International Union of Basic and Clinical Pharmacology References: 1. Nutt DJ. Beyond psychoanaleptics - can we improve antidepressant drug nomenclature? [published correction appears in J Psychopharmacol. 2009 Sept;23(7):861]. J Psychopharmacol. 2009;23(4):343-345. doi:10.1177/0269881109105498 2. Zohar J, Stahl S, Moller HJ, et al. A review of the current nomenclature for psychotropic agents and an introduction to the Neuroscience-based Nomenclature. Eur Neuropsychopharmacol. 2015;25(12):2318-2325. doi:10.1016/j.euroneuro.2015.08.019 Objectives As NbN is a novel classification system that can be used as a teaching tool as well as for other purposes, we aimed to understand the experience, views and needs of the psychiatric trainees and early career psychiatrists who will shape the future of psychiatry, around drug classification systems. Methods The ethical clearance of the study was obtained from King’s College London. We prepared an online survey (https://forms.gle/FCSdVTFH4U5QNn5t8) with a multinational group of early career pscyhiatrists who met through the CINP and EFPT, and test-run the survey with a small group of psychiatric trainees. The online survey was then disseminated via emailing lists and groups of early careers psychiatrists as well as through social media. Results At the time of this abstract submission, the data collection is ongoing. Results will include analyses of the experience with different drug classifcations systems, awareness, views and attainment of NbN, stratified according to the demographic data (country, careers status, main work setting). Conclusions The findings from this study will shed light on the views and needs of early career psychiatrists on the topic from clinical and academic aspects, a previously unexplored perspective on drug classification systems. The findings can inform the planning of various strategies to address areas to improve the use and teaching of these tools. Disclosure of Interest None Declared
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".