Exploring the association between the presence and characteristics of pain and lifetime depression in adolescents: A cross sectional ABCD study analysis
Bibliographic record
Abstract
IntroductionDepression and pain co occur, even during adolescence. However, there is limited knowledge on the association between pain and lifetime depression in community samples, and which biopsychosocial factors are associated with this co occurrence. MethodsCross sectional analysis of the Adolescent Brain and Cognitive Development (ABCD) two year follow up. We explored associations between the presence and characteristics of pain (intensity, duration, activity limitations, and number of pain sites) and lifetime depression using logistic regression. We explored associations of brain structure, physical, behavioural, emotional, social, and cognitive factors with lifetime depression and pain compared to having had one or neither condition using multinomial logistic regression. ResultsA total of 5,211 adolescents (mean age=12.0 years) who had: (1) no lifetime mental ill health and no pain (n=3,327); (2) pain only (n=1,407); (3) lifetime depressive disorder but no pain (n=272); and (4) lifetime depressive disorder and pain (n=205) were included. Pain presence was associated with lifetime depression (OR[95%CI]: 1.76 [1.45, 2.13], p<0.001). Pain-related activity limitations (1.13 [1.06, 1.21], p<0.001) and the number of pain sites (1.06 [1.02, 1.09], p<0.001) were associated with lifetime depression. Various behavioural, emotional, social, and cognitive, but not brain structure or physical factors, were associated with lifetime depression and pain. LimitationsFuture longitudinal analyses should validate the benefit of prognostic markers on predicting co occurring depression and pain. ConclusionsThese results support an association between the presence and characteristics of pain and lifetime depression during adolescence, and the need for more integrated clinical care of youth experiencing both depression and pain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".