Biomarkers of treatment-resistant schizophrenia: A systematic review
Bibliographic record
Abstract
Treatment-resistant schizophrenia (TRS) is associated with great disability, functional impairment, and substantial socioeconomic costs. While clozapine is indicated in patients with TRS, its use is restricted to patients who have not responded to at least 2 other antipsychotics, thus implying a series of empirical trials of different drugs before receiving effective treatment. In this scenario, the identification of reliable biological markers to predict the risk for TRS before starting pharmacological treatments might significantly improve the management of TRS in its early stages. We conducted a systematic review on PubMed, Scopus and Web of Science to identify studies investigating peripheral biological markers of TRS. A total of 75 articles were included. These studies mostly investigated the association between TRS and genetic markers (n = 42, of which 16 with a genome-wide and 25 with a candidate-gene design) and protein/metabolite markers (n = 23), while only a minority of studies investigated RNA markers (n = 5), methylation levels (n = 4), gut microbiota profiles (n = 1), or more than one type of marker (n = 3). The elucidation of peripheral biomarkers of TRS is challenging due to the large heterogeneity across studies in terms of clinical definition of TRS, the relatively small sample size of many studies, as well as the lack of powered studies integrating data at a multi-omic level. Nonetheless, available studies suggest TRS to be a trait with a significant heritability and point to inflammation and cytokine imbalance as the most promising pathways involved in this complex phenotype.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".