A Neuroscience-Based Nomenclature (NbN) for Psychotropic Agents
Bibliographic record
Abstract
On January 1, 2017, the International Journal of Neuropsy chopharmacology will adopt the Neuroscience-based-Nomenclature (NbN). This will mark the culmination of a process that began in 2008 and involved a working group of members from 5 scientific organizations (the American, Asian, European, and International Colleges of Neuropsychopharmacology, as well as the International Union of Basic and Clinical Pharmacology). This group was tasked with building a classification system for psychotropic agents that would meet the requirements of a rational nomenclature. The expectations were that it would: (1) be based on contemporary knowledge, (2) help clinicians make informed decisions when choosing a first or subsequent pharmacological intervention, (3) provide a naming system that does not conflict with the use of medications, and (4) be capable of accommodating new types of compounds. The impetus for this initiative came from the realization that our existing nomenclature has been overtaken by science and clinical reality. In the 1950s when the therapeutic benefits of chlorpromazine and imipramine were discovered in psychosis and depression, respectively, they were subsequently designated as antipsychotic and antidepressant medications. There was no need for a more complex nomenclature at that time. However, this scheme rapidly became obsolete, because it was observed that some of these medications were effective in other brain disorders. For instance, in the 1970s the efficacy of the antidepressant chlorimipramine was extended to obsessions and compulsions. In …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".