Developing a clinical decision tool based on electroretinogram to monitor the risk of severe mental illness
Bibliographic record
Abstract
BACKGROUND: We have shown that electroretinograms can discriminate between patients with severe mental illness (SMI) and healthy controls in previous studies. We now intend to enhance the development and clinical utility of ERG as a biological tool to monitor the risk of SMI. METHODOLOGY: A sample of 301 SMI patients (bipolar disorder or schizophrenia) and 200 controls was first split into a training (N = 401) and testing dataset (N = 100). A logistic regression using ERG was modeled in the training data, while external validation and discriminative ability were assessed in the testing data. A decision curve analysis was used to test clinical usefulness. Moreover, the identification of thresholds of uncertainty based on the two-graph ROC and the interval of uncertainty was used to enhance prediction. RESULTS: The discriminative assessment of the ERG showed very high sensitivity (91%) and specificity (89%) after considering uncertainty levels. Furthermore, for prediction probabilities ranging from 0.14 to 0.95 in the testing data, the net benefit of using our ERG model to decide whether to intervene or not exceeded that of never or always intervening. CONCLUSION: The ERG predicted SMI risk with a high level of accuracy when uncertainty was accounted for. This study further supports the potential of ERG to become a useful clinical decision tool to decide the course of action for subjects at risk of SMI. However, further investigation is still needed in longitudinal studies to assess the external validity of the instrument.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".