Bibliographic record
Abstract
Given that we are celebrating the 50th birthday of neuroleptics introduction in psychiatry, the author proposes to take a look at certain results related to therapeutic practice. After a brief chronological literature review of the clinical practices and theoretical models that have controlled drug treatment of schizophrenia, the author presents a critical review of four meta-analyses. Since Delay, Deniker and Harl's initial report, the story of neuroleptics comprises several periods. In 1963, the hyper-dopaminergic theory of psychoses was proposed. Another period began with models mainly based on the serotonin/dopamine relative blockade receptor hypothesis. More recently, a new framework to understand the differential effect of antipsychotics is related to the appropriate modulation (e.g., fast dissociation) of the D2 receptor alone. The concept of atypicality has become a new vista for research and to market new compounds. However, after 50 years of neuroleptic drugs, are we able to answer the following simple questions: Are neuroleptics effective in treating schizophrenia? Is there a difference between atypical and conventional neuroleptics? How do the efficacy and safety of newer antipsychotic drugs compare with those of clozapine? Actually, the answers yielded by these simple questions by meta-analysis should elicit in us a good deal of humility. If we wish to base psychiatry on evidence-based medicine, we run a genuine risk in taking a closer look at what has long been considered fact. Each psychiatrist must continue to be critical, sceptical, optimistic (not overoptimistic) and to learn in order to integrate the positive aspects of our growing knowledge base.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".