Half a century of research on antipsychotics and schizophrenia: A scientometric study of hotspots, nodes, bursts, and trends
Bibliographic record
Abstract
Changes over 50 years of research on antipsychotics in schizophrenia have occurred. A scientometric synthesis of such changes over time and a measure of researchers' networks and scientific productivity is currently lacking. We searched Web of Science Core Collection from inception until November 5, 2021, using the appropriate key. Our primary objective was to conduct systematic mapping with CiteSpace to show how clusters of keywords have evolved over time and obtain clusters' structure and credibility. Our secondary objective was to measure research network performance (countries, institutions, and authors) using CiteSpace, VOSviewer, and Bibliometrix. We included 32,240 studies published between 1955 and 2021. The co-cited reference network identified 25 clusters with a well-structured network (Q=0.8166) and highly credible clustering (S=0.91). The main trends of research were: 1) antipsychotic efficacy; 2) cognition in schizophrenia; 3) side effects of antipsychotics. Last five years research trends were: 'ultra-resistance schizophrenia' (S=0.925), 'efficacy/dose-response' (S=0.775), 'evidence-synthesis' (S=0.737), 'real-world effectiveness' (S=0.794), 'cannabidiol' (S=0.989), and 'gut microbiome' (S=0.842). These results can inform funding agencies and research groups' future directions.
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 | 0.024 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.131 | 0.232 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".