Association Between Intellectual Disability and Hair Cortisol Concentration in Adolescents in a Brazilian Population‐Based Birth Cohort
Bibliographic record
Abstract
OBJECTIVE: Children with intellectual disability (ID) usually exhibit neuroendocrine functioning impairment, such as alterations in the hypothalamic-pituitary-adrenal (HPA) neuroendocrine axis, which can result in glucocorticoid cortisol release alterations. Indeed, many studies showed a positive association between ID and cortisol concentration. However, evidence is lacking on the relationship between child neurodevelopment and cortisol levels during adolescence in population-based studies. We aimed to test the association between ID and hair cortisol concentration (HCC) during adolescence in a prospective population-based cohort. METHODS: Data from 1770 individuals in the 2004 Pelotas Birth Cohort were used. ID was diagnosed at age 6 through clinical examination. Hair cortisol was measured at age 15. Association was assessed using linear regression models adjusted for sex, socio-economic factors, hair-related variables and corticosteroid use. RESULTS: Higher HCC were observed in individuals with ID (β: 1.120; 95% CI: 1.012, 1.241) in the analysis adjusted for sex, hair-related variables and corticosteroid use. Compared to the other aetiological groups, this was more evident in idiopathic ID. But this did not remain significant when demographics/socio-economic variables were controlled for. CONCLUSION: Children with ID, particularly those with idiopathic ID, might exhibit dysfunction in the HPA axis or experience heightened stress levels during adolescence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".