Neural correlates of emotion dysregulation in adolescents: a systematic review
Bibliographic record
Abstract
aberrant emotion regulation processes as distinct outcomes.We made this decision because we want to know what studies that have defined the clinical outcome as ED have identified about its underlying mechanisms.The practical application here is that clinical settings do not measure specific problems with emotion regulation directly, however they regularly report on whether ED is present or not.The large number of available measures of ED is problematic for the field, as such large diversity in measurement combined with the lack of standardization of the definition of ED leaves room for misalignment between what we define as ED and what we may be measuring using these scales [1,5].Freitag et al. note that the majority of studies about childhood ED have included one of five measures of ED and each of these measures include between 10 and 36 items assessing both the "internal (cognitive or emotional) processes" and behavioural manifestations of these processes.These five most commonly used scales include the Children's Emotion Management Scales (CEMS) [9-11], Cognitive Emotion Regulation Questionnaire (CERQ) [12,13], Difficulties in Emotion Regulation Scale (DERS) [14-16], Emotion Regulation Checklist (ERC) [17], and the Emotion Regulation Questionnaire (ERQ).Internal processes refer to implicit and explicit regulation strategies like attention deployment and cognitive reappraisal.There is reason for guarded optimism in regards to the reliability of clinical measures of ED, however, as these five common measures of ED demonstrate moderate to high internal consistencies across different clinical samples of young people [1].As such, this systematic review has two aims: First, to synthesize the available literature exploring neural correlates of ED.The second aim is to explore how the neural correlates of ED are similar or different across different clinical groups and healthy controls.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".