What’s in a name? The evolution of the nomenclature of antipsychotic drugs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Psychiatry as a science and psychotherapy as an art thrive on words, words that were often coined arbitrarily and that are often used idiosyncratically. This article examines the origins, progenitors and usage of the word "antipsychotic" and explores its ramifications. METHODS: Original publications from the 1950s onward, beginning with the report of the discovery of chlorpromazine, were sought for their specific references to the terminology of drugs used to treat psychotic disorders. Preferences for individual words, debates surrounding their adoption and changing trends in their use are reviewed from scientific, clinical and social perspectives. RESULTS: Over the past 50 years the drugs used in the treatment of schizophrenia and other psychotic disorders have been variously labelled "tranquillizers," "neuroleptics," "ataractics," "antipsychotics" and "anti-schizophrenic agents." These terms, coined out of necessity, were quickly accepted with little debate or due consideration of their clinical, personal and social implications. The development of a new generation of antipsychotic drugs as well as the prospect of treatment strategies with diverse mechanisms of action highlight the need to re-examine the issues involved in the naming, classification and labelling of psychotropic drugs in general and of "antipsychotics" in particular. CONCLUSION: This historical overview of the labelling of drugs used in the treatment of psychoses reflects the confusion and controversy surrounding the naming and classification of drugs and diseases in general. It also illustrates the dynamic interplay of personal beliefs, rational thinking, practical considerations and societal values in shaping the scientific process.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it