Treating Negative Symptoms in Schizophrenia: an Update
Bibliographic record
Abstract
Interest in the negative symptoms of schizophrenia has increased rapidly over the last several decades, paralleling a growing interest in functional, in addition to clinical, recovery, and evidence underscoring the importance negative symptoms play in the former. Efforts continue to better define and measure negative symptoms, distinguish their impact from that of other symptom domains, and establish effective treatments as well as trials to assess these. Multiple interventions have been the subject of investigation, to date, including numerous pharmacological strategies, brain stimulation, and non-somatic approaches. Level and quality of evidence vary considerably, but to this point, no specific treatment can be recommended. This is particularly problematic for individuals burdened with negative symptoms in the face of mild or absent positive symptoms. Presently, clinicians will sometimes turn to interventions that are seen as more "benign" and in line with routine clinical practice. Strategies include use of atypical antipsychotics, ensuring the lowest possible antipsychotic dose that maintains control of positive symptoms (this can involve a shift from antipsychotic polypharmacy to monotherapy), possibly an antidepressant trial (given diagnostic uncertainty and the frequent use of these drugs in schizophrenia), and non-somatic interventions (e.g., cognitive behavioral therapy, CBT). The array and diversity of strategies currently under investigation highlight the lack of evidence-based treatments and our limited understanding regarding negative symptoms underlying etiology and pathophysiology. Their onset, which can precede the first psychotic break, also means that treatments are delayed. From this perspective, identification of biomarkers and/or endophenotypes permitting earlier diagnosis and intervention may serve to improve treatment efficacy as well as outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".