INTEGRATION OF COMPLEMENTARY BIOMARKERS IN PATIENTS WITH FIRST EPISODE PSYCHOSIS: RESEARCH PROTOCOL OF A PROSPECTIVE FOLLOW UP STUDY
Bibliographic record
Abstract
In this project, we recruited a sample of 150 patients with first episode of psychosis with schizophrenia features (FEP) and 100 healthy controls. We assessed the differences between these two groups, as well as the changes between the acute phase of illness and subsequent remission among patients over 18-month longitudinal follow-up. The assessments were divided into four work packages (WP): WP1- psychopathological status, neurocognitive functioning and emotional recognition; WP2- stress response measured by saliva cortisol during a stress paradigm; cerebral blood perfusion in the resting state (with single photon emission computed tomography (SPECT) and during activation paradigm (with Transcranial Ultrasonography Doppler (TCD); WP3-post mortem analysis in histologically prepared human cortical tissue of post mortem samples of subjects with schizophrenia in the region that synaptic alteration was suggested by WP1 and WP2; WP4- pharmacogenetic analysis (single gene polymorphisms and genome wide association study (GWAS). We expect that the analysis of these data will identify a set of markers that differentiate healthy controls from patients with FEP, and serve as an additional diagnostic tool in the first episode of psychosis, and prediction tool which can be then used to help tailoring individualized treatment options. In this paper, we describe the project protocol including aims and methods and provide a brief description of planned post mortem studies and pharmacogenetic analysis.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".