Electrodermal responding predicts responses to, and may be altered by, preschool intervention for ADHD.
Bibliographic record
Abstract
OBJECTIVES: To evaluate electrodermal activity (EDA) as a prospective biomarker of treatment response, to determine whether patterns of EDA are altered by treatment, and to assess oppositional defiant disorder (ODD) as a possible moderator of trajectories in EDA after an empirically supported behavioral intervention for attention-deficit hyperactivity disorder (ADHD) in preschool. METHOD: Nonspecific fluctuations (NSFs) in skin conductance, which index sympathetic nervous system activity, were assessed among 4-6 year old children with ADHD (n = 99) before they participated with their parents in 1 of 2 versions of the Incredible Years intervention. All were reassessed at posttreatment, and a subgroup (n = 49) were assessed again at 1-year follow-up. RESULTS: No difference in pretreatment NSFs was observed between ADHD participants and a group of normal control children (n = 41). Nevertheless, among those with ADHD, fewer NSFs at pretest predicted poorer treatment response on 4 of 7 externalizing outcomes. Furthermore, treatment was associated with increasing NSFs across time, but not for those who scored high on ODD at pretest. CONCLUSIONS: Low EDA appears to mark resistance to treatment among preschoolers with ADHD. Furthermore, although our study was not experimental, treatment was associated with longitudinal increases in EDA, which were not observed in a normal control group. This may suggest increased sensitivity to discipline, with positive implications for long term outcome. In contrast to treated participants as a whole, however, those who scored high on ODD at pretest exhibited reduced EDA over time. (PsycINFO Database Record
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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 | 0.005 |
| 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 | 0.001 |
| 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".