Thirty years of research on negative symptoms of schizophrenia: A scientometric analysis of hotspots, bursts, and research trends
Bibliographic record
Abstract
Research on negative symptoms of schizophrenia has received renewed interest since the 1980s. A scientometric analysis that objectively maps scientific knowledge, with changes in recent trends, is currently lacking. We searched the Web of Science Core Collection (WOSCC) on December 17, 2021 using relevant keywords. R-bibliometrix and CiteSpace were used to perform the analysis. We retrieved 27,568 references published between 1966 and 2022. An exponential rise in scientific interest was observed, with an average annual growth rate in publications of 16.56% from 1990 to 2010. The co-cited reference network that was retrieved presented 24 different clusters with a well-structured network (Q=0.7921; S=0.9016). Two distinct major research trends were identified: research on the conceptualization and treatment of negative symptoms. The latest trends in research on negative symptoms include evidence synthesis, nonpharmacological treatments, and computational psychiatry. Scientometric analyses provide a useful summary of changes in negative symptom research across time by identifying intellectual turning point papers and emerging trends. These results will be informative for systematic reviews, meta-analyses, and generating novel hypotheses.
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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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.114 | 0.165 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".