Prenatal maternal stress from a natural disaster predicts dermatoglyphic asymmetry in humans
Bibliographic record
Abstract
Dermatoglyphic asymmetry of fingertip ridge counts is more frequent in schizophrenia patients than normal controls, and may reflect disruptions in fetal development during Weeks 14-22 when fingerprints develop. However, there are no data in humans linking specific adverse events at specific times to dermatoglyphic asymmetries. Our objective was to determine whether prenatal exposure to a natural disaster (1998 Quebec ice storm) during Weeks 14-22 would result in increased dermatoglyphic asymmetry in children, and to determine the roles of maternal objective stress exposure, subjective stress reaction, and postdisaster cortisol. Ridge counts for homologous fingers were scored for 77 children (20 target exposed [Weeks 14-22] and 57 nontarget exposed [exposed during other gestation weeks]). Children in the target group had more than 0.50 SD greater asymmetry than the nontarget group. Within the target group, children whose mothers had high subjective ice storm stress had significantly greater asymmetry than those with lower stress mothers, and maternal postdisaster cortisol had a significant negative correlation with the children's dermatoglyphic asymmetry (r = -.56). Prenatal maternal stress during the period of fingerprint development results in greater dermatoglyphic asymmetry in their children, especially in the face of greater maternal distress.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".