Accuracy of episodic autobiographical memory in children with early thyroid hormone deficiency using a staged event
Bibliographic record
Abstract
Autobiographical memory (AM) is a highly constructive cognitive process that often contains memory errors. No study has specifically examined AM accuracy in children with abnormal development of the hippocampus, a crucial brain region for AM retrieval. Thus, the present study investigated AM accuracy in 68 typically and atypically developing children using a staged autobiographical event, the Children's Autobiographical Interview, and structural magnetic resonance imaging. The atypically developing group consisted of 17 children (HYPO) exposed during gestation to insufficient maternal thyroid hormone (TH), a critical substrate for hippocampal development, and 25 children with congenital hypothyroidism (CH), who were compared to 26 controls. Groups differed significantly in the number of accurate episodic details recalled and proportion accuracy scores, with controls having more accurate recollections of the staged event than both TH-deficient groups. Total hippocampal volumes and anterior hippocampal volumes were positively correlated with proportion accuracy scores, but not total accurate episodic details, in HYPO and CH. In addition, greater severity of TH deficiency predicted lower proportion accuracy scores in both HYPO and CH. Overall, these results indicate that children with early TH deficiency have deficits in AM accuracy and that the anterior hippocampus may play a particularly important role in accurate AM retrieval.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".