Cortical morphology and early adverse birth events in men with first-episode psychosis
Bibliographic record
Abstract
BACKGROUND: Reduced cortical gray-matter volume is commonly observed in patients with psychosis. Cortical volume is a composite measure that includes surface area, thickness and gyrification. These three indices show distinct maturational patterns and may be differentially affected by early adverse events. The study goal was to determine the impact of two distinct obstetrical complications (OCs) on cortical morphology. METHOD: A detailed birth history and MRI scans were obtained for 36 patients with first-episode psychosis and 16 healthy volunteers. RESULTS: Perinatal hypoxia and slow fetal growth were associated with cortical volume (Cohen's d = 0.76 and d = 0.89, respectively) in patients. However, the pattern of associations differed across the three components of cortical volume. Both hypoxia and fetal growth were associated with cortical surface area (d = 0.88 and d = 0.72, respectively), neither of these two OCs was related to cortical thickness, and hypoxia but not fetal growth was associated with gyrification (d = 0.85). No significant associations were found within the control sample. CONCLUSIONS: Cortical dysmorphology was associated with OCs. The use of a global measure of cortical morphology or a global measure of OCs obscured important relationships between these measures. Gyrification is complete before 2 years and its strong relationship with hypoxia suggests an early disruption to brain development. Cortical thickness matures later and, consistent with previous research, we found no association between thickness and OCs. Finally, cortical surface area is largely complete by puberty and the present results suggest that events during childhood do not fully compensate for the effects of early disruptive events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".