Auxiliary differential diagnosis of schizophrenia and phases of bipolar disorder based on the blood serum Raman spectra
Bibliographic record
Abstract
Abstract Schizophrenia (SZ) and bipolar disorder (BP) are severe psychiatric disorders that are characterized by an extensive spectrum of symptoms and affect approximately 2% of the world population. BP exhibits three well‐distinct phases, which are classified as manic and depressive episodes and euthymic phase. These disorders are of difficult differential clinical diagnosis due to the similarity of their symptoms. Diagnostic approaches for SZ and BP are based on constructed patient interviews and subjective evaluations of clinical symptoms, and there are still no molecular‐based auxiliary diagnostic tools to support the clinical diagnosis. In this study, an analytical model for auxiliary differential diagnosis of SZ and BP, based on the analysis of patients' blood serum Raman spectra, is developed, which is able to account for the different BP phases and can also differentiate SZ and BP patients from healthy individuals. The model is based on a hierarchical sequence of four two‐class PLS‐DA steps where the Raman spectra are the X‐predictor variables. It is concluded that the full 400–3,100 cm−1 Raman spectroscopic range is a sensitive probe for the disorders, thus working as a general spectroscopic biomarker for the illnesses. The proposed methodology is reliable, fast, cheap, essentially minimal‐invasive, and might be implemented easily in the clinical environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".