Addressing the unmet needs of patients with persistent negative symptoms of schizophrenia: emerging pharmacological treatment options
Bibliographic record
Abstract
The negative symptoms of schizophrenia represent an impairment of normal emotional responses, thought processes and behaviors, and include blunting or flattening of affect, alogia/aprosody, avolition/apathy, anhedonia, and asociality. Negative symptoms contribute to a reduced quality of life, increased functional disability, increased burden of illness, and poorer long-term outcomes, to a greater degree than positive symptoms. Primary negative symptoms are prominent and persistent in up to 26% of patients with schizophrenia, and they are estimated to occur in up to 58% of outpatients at any given time. Negative symptoms respond less well to medications than positive symptoms, and to date treatment options for negative symptoms have been limited, with no accepted standard treatment. Modest benefits have been reported with a variety of different agents, including second-generation antipsychotics and add-on therapy with antidepressants and other pharmacological classes. Recent clinical research focusing on negative symptoms target novel biological systems, such as glutamatergic neurotransmission. Different approaches include: enhancing N-methyl-D-aspartate receptor function with agents that bind directly to the glycine ligand site or with glycine reuptake inhibitors; influencing the metabotropic glutamate receptor (mGluR2/3) with positive allosteric modulators; and stimulating nicotinic acetylcholine receptors. In conclusion, the lack of clearly efficacious pharmacological treatments for the management of negative symptoms represents a significant unmet need, especially considering the importance of these symptoms on patient outcomes. Hence, further research to identify and characterize novel pharmacological treatments for negative symptoms is greatly needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".