Oculomotor Abnormalities and Aberrant Neuro-Developmental Markers: Composite Endophenotype for Bipolar I Disorder: Anomalies Oculomotrices et Marqueurs Neuro-Développementaux Aberrants : Endophénotype Composite du Trouble Bipolaire I
Bibliographic record
Abstract
BACKGROUND: Neurological soft signs (NSSs), minor physical anomalies (MPAs), and oculomotor abnormalities were plausible biomarkers in bipolar disorder (BD). However, specific impairments in these markers in patients after the first episode mania (FEM), in comparison with first-degree relatives (high risk [HR]) of BD and healthy subjects (health control [HC]) are sparse. AIM OF THE STUDY: This study aimed at examining NSSs, MPAs, and oculomotor abnormalities in remitted adult subjects following FEM and HR subjects in comparison with matched healthy controls. Investigated when taken together, could serve as composite endophenotype for BD. METHODS: = 0.70) using neurological evaluation scale, Waldrop's physical anomaly scale and eye tracking (SPEM) and antisaccades (AS) paradigms, respectively. RESULTS: Significant differences were found between groups on NSSs, MPAs, and oculomotor parameters. Abnormalities are higher in FEM subjects compared to HR and HC subjects. Using linear discriminant analysis, all 3 markers combined accurately classified 72% of the original 82 subjects (79·2% BD, 56·70% HR, and 82·1% HC subjects). CONCLUSIONS: AS and SPEM could enhance the utility of NSSs, and MPAs as markers for BD. The presence of these abnormalities in FEM suggests their role in understanding the etiopathogenesis of BD in patients who are in the early course of illness. These have the potential to be composite endophenotypes and have further utility in early identification in BD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".