Additional file 1 of Predicting the risk and timing of major mood disorder in offspring of bipolar parents: exploring the utility of a neural network approach
Bibliographic record
Abstract
Additional file 1: Table S1. Example of discrete survival data for hypothetical scenario with sex and blood pressure as covariates. Figure S1. Feed-forward neural network with one hidden layer. Table S2. PLANN one-, three-, five-year prediction assessments showing optimal threshold, accuracy, specificity (spec), sensitivity (sens), positive predictive value (PPV), total number of individuals in the risk set (n) and total number of events observed within each time interval, determined using tenfold cross-validation. Table S3. Discrete survival model one-, three-, five-year prediction assessments showing optimal threshold, accuracy, specificity (spec), sensitivity (sens), positive predictive value (PPV), total number of individuals in the risk set(n) and total number of events observed within each time interval, determined using tenfold cross-validation. Table S4. Number of subthreshold diagnoses for individuals in the full sample, individuals who experienced the major mood disorder outcome (diagnosis of BP, MDD and/or schizoaffective disorder), and individuals who did not experience the outcome.
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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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.773 | 0.086 |
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".