Subtyping Schizophrenia by Treatment Response: Antipsychotic Development and the Central Role of Positive Symptoms
Bibliographic record
Abstract
We have recently proposed a model for subtyping schizophrenia based on antipsychotic (AP) treatment response. Evidence suggests that APs, both old and new, are comparable in terms of efficacy; however, one AP, clozapine, is uniquely effective in one subgroup of patients (that is, those with treatment-resistant schizophrenia [TRS]). This permits us to subdivide schizophrenia into 3 specific groups: AP responsive, clozapine responsive, and clozapine resistant. Here, we integrate this model with current criteria related to TRS and ultraresistant schizophrenia, the latter referred to in our model as clozapine resistant. We suggest several modifications to existing criteria, in line with current evidence and practice patterns, particularly emphasizing the need to focus on positive symptoms. While APs can favourably impact numerous dimensions related to schizophrenia, it is their effect on positive symptoms that distinguishes them from other psychotropics. Further, it is positive symptoms that are central to AP and clozapine resistance, and it is these people that place the greatest demands on acute and long-term inpatient resources. In moving AP development forward, we advocate specifically focusing on positive symptoms and capitalizing on the evidence we have of 3 subtypes of psychosis (that is, positive symptoms) based on treatment response, implicating 3 distinguishable forms of underlying pathophysiology. Conversely, pooling these groups risks obfuscating potentially identifiable differences. Such a position does not challenge the importance of dopamine D2 receptor blockade, but rather highlights the need to better isolate those other subgroups that require something more or entirely different.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".