Transdiagnostic biomarker approaches to mental health disorders: Consideration of symptom complexity, comorbidity and context
Bibliographic record
Abstract
Depression is a multifaceted disorder characterized by heterogeneous symptom profiles and high rates of comorbidity with other commonly occurring mental illnesses. Considering the burden of mental health disorders and the lack of efficacy of available treatments, there is a need for biomarkers to predict tailored or personalized treatments. However, identifying reliable biomarkers for complex mental illnesses, such as depression, anxiety and PTSD, has been challenging, likely owing to the heterogeneity, comorbidity and differences in experiences and histories of individuals. For these reasons, taking a transdiagnostic approach, which identifies biomarkers that map onto shared symptoms/constructs across disorders could be most effective for informing personalized or precision medicine approaches in psychiatry. Transdiagnostic features of anxiety, depression and anhedonia have been examined in relation to brain activity and connectivity patterns. Neuroendocrine and inflammatory markers, which are altered in depression and other comorbid illness, such as post-traumatic stress disorder (PTSD), might be useful in differentiating transdiagnostic symptom profiles as well as treatment responses. Ultimately, biomarker research that looks beyond diagnostic categories and embraces the complexity of individuals' lives and experiences might be more effective in moving towards precision medicine in psychiatry.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".