Bibliographic record
Abstract
OBJECTIVE: Neuroleptics were introduced into North America 60 years ago. The credit for this advance is generally accorded to Heinz Lehmann. I sought to explore whether Lehmann really was the first North American psychiatrist to study the effects of chlorpromazine (CPZ) and to provide a more balanced view of its application in a clinical context. METHOD: I searched for historical documents and published articles in several libraries and interviewed psychiatrists active from 1952-1970. RESULTS: The first article in English was published in the July volume of the Archives of Neurology and Psychiatry in 1954 (n=71). Another article, written in French by Roland Saucier and published in a journal called Le Saguenay Médical, also described the effects of CPZ on a Canadian psychiatric population in August 1954 (n>200). However, the first prescription for CPZ was written by Roland Saucier, who brought the product back from Paris after a fellowship there. Ruth Kajander, in Ontario, was also one of the first prescribers of this drug, following her study of its use in anesthesia and a publication in the proceedings of a symposium. CONCLUSION: The contents of the 2 naturalistic studies were compared. Lehmann's study started 1 month before that of Saucier. Lehmann was the first North American psychiatrist to publish an article on CPZ, but Roland Saucier nevertheless made an important contribution, being the first to prescribe this drug in North America and reporting results for a study with a sample size 3 times that of Lehmann's study.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".