The Electroretinogram as a Potential Biomarker of Psychosis in Children at Familial Risk
Bibliographic record
Abstract
Abstract We previously proposed the electroretinogram (ERG) as a promising biomarker of major psychiatric disorders such as schizophrenia (SZ) and bipolar disorder (BP), given that we found anomalies in the ERG parameters of patients with these diagnoses as well as in their children who are at high risk (HR) of developing such disorders. The aim of the present study is to investigate the usefulness of the ERG for individual detection, among HR children, of an ERG profile resembling that of a SZ patient, as this may indicate a stronger likelihood of transition to psychosis. Using a logistic regression model previously derived from the ERG assessments of SZ patients and control (CT) subjects, individual risk scores were obtained for 61 HR and 80 CT youth. Those with a very high individual risk score were classified as “schizophrenia-like” (SZ-like). We found that the HR subjects were 3.5 times more likely to be classified as SZ-like than the CT subjects (95% CI [1.1–11.8]). Furthermore, among the HR subjects, we studied the relationship between the SZ-like classification and psychotic-like experiences and found that HR subjects classified as SZ-like were 2.7 times more likely than all remaining HR subjects to have experienced psychotic-like symptoms (95% CI [1.3–4.6]), and 6.8 times more likely than those with a very low individual risk score (95% CI [1.4–40.4]). Our results suggest that a model previously derived from ERG data on SZ patients could be a potential tool for early detection of the susceptibility to a psychotic-like disorder among familial HR children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".