Effects of Early Thyroid Hormone Deficiency on Children's Autobiographical Memory Performance
Bibliographic record
Abstract
Memory deficits and hippocampal abnormalities have been described in individuals with thyroid hormone (TH) insufficiencies; however, no study has yet examined their autobiographical memory (AM) abilities, which are known to be compromised by hippocampal damage. Investigations in adults have shown that AM consists of both episodic and semantic components and that the hippocampus is preferentially involved in episodic AM. The present study used the Children's Autobiographical Interview (CAI) to study episodic and semantic AM in 79 children aged 9 to 14 years, including 26 with early-treated congenital hypothyroidism (CH), 23 born to women with inadequately treated hypothyroidism during pregnancy (HYPO), and 30 typically developing controls. Results showed that relative to controls, CH and HYPO groups both exhibited weaknesses in episodic AM, but not semantic AM. In particular, CH and HYPO groups showed difficulty in recalling event details (i.e., the main happenings) and visual details from past experiences. Overall, this study highlights the importance of TH for early neurodevelopment and provides critical new insight into the effects of early treated TH deficiency on long-term memory performance. Furthermore, the present study indicates that the CAI is an effective tool for investigating episodic AM impairment in clinical pediatric populations.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".