QEEG and LORETA findings in children with histories of relational trauma.
Bibliographic record
Abstract
Abuse and neglect occurring in childhood have been associated with a number of functional and physiological effects on the brain. This study extends previous research that investigated the quantitative electroencephalogram (qEEG) patterns in children with histories of relational trauma through the inclusion of additional participants and measures. As in previous studies, the relative power, absolute power, and coherence values in children with histories of abuse were compared to the Neuroguide database. Results did not show any significant differences in relative or absolute power in the theta range. Similarly, there were no significant coherence differences. Database comparisons were also made using low resolution electromagnetic tomography (LORETA) in order to determine which sub-cortical brain structures may be affected by abuse or trauma, though there were no significant differences in any frequency (0-30Hz). A review of the literature suggests that the prevalence of mu in normal adults and children ranges from 0 to 19%. The present study found a mu prevalence rate of 60.6% in the children who experienced abuse or neglect. Finally, comparisons were made between participants who demonstrate a mu pattern and those who do not to determine if this pattern is associated with certain behavioral and/or attention problems as assessed by the Child Behavior Checklist (CBCL) and the Tests of Variables of Attention (TOVA), respectively. There were no significant differences between children with a mu pattern versus children who did not exhibit a mu pattern on the Social Problems, Thought Problems, or Attention subscale scores on the CBCL or on the Commission subscale score on the TOVA.
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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 | 0.002 |
| 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 | 0.001 |
| 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".