Dissecting negative symptoms in schizophrenia: Opportunities for translation into new treatments
Bibliographic record
Abstract
Among the constellation of symptoms that characterize schizophrenia, negative symptoms have emerged as a critical feature linked to the functional impairment experienced by affected individuals. Despite advances in our understanding of the role of negative symptoms in the illness, effective treatments for these debilitating symptoms have remained elusive. In this review we explore the contemporary conceptualization of negative symptoms in schizophrenia, including the identification of two key subdomains of diminished expression and amotivation, and clarifications around hedonic capacity. We then explore strategies for clinical assessments of negative symptoms, followed by findings using objective paradigms for evaluating discrete aspects of these negative symptoms in clinical populations and animal models, both for symptoms of diminished expression and within the multifaceted motivation system. We conclude with a consideration of current strategies for drug development for these negative symptoms, the role of heterogeneity in the clinical presentation of symptoms in schizophrenia and opportunities for personalized assessment and treatment approaches, as well as a commentary on current clinical drug trial design and the role of environmental opportunities for novel treatments to effect change and improve outcomes for affected individuals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".