Psychological predictors of headache remission in children and adolescents
Bibliographic record
Abstract
OBJECTIVE: Longitudinal studies on headaches often focus on the identification of risk factors for headache occurrence or "chronification". This study in particular examines psychological variables as potential predictors of headache remission in children and adolescents. METHODS: Data on biological, social, and psychological variables were gathered by questionnaire as part of a large population-based study (N=5,474). Children aged 9 to 15 years who suffered from weekly headaches were selected for this study sample, N=509. A logistic regression analysis was conducted with remission as the dependent variable. In the first step sex, age, headache type, and parental headache history were entered as the control variables as some data already existed showing their predictive power. Psychological factors (dysfunctional coping strategies, internalizing symptoms, externalizing symptoms, anxiety sensitivity, somatosensory amplification) were entered in the second step to evaluate their additional predictive value. RESULTS: Highly dysfunctional coping strategies reduced the relative probability of headache remission. All other selected psychological variables reached no significance, ie, did not contribute additionally to the explanation of variance of the basic model containing sex and headache type. Surprisingly, parental headache and age were not predictive. The model explained only a small proportion of the variance regarding headache remission (R(2) =0.09 [Nagelkerke]). CONCLUSION: Successful coping with stress in general contributed to remission of pediatric headache after 2 years in children aged between 9 and 15 years. Psychological characteristics in general had only small predictive value. The issue of remission definitely needs more scientific attention in empirical studies.
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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 | 0.004 |
| 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 | 0.001 |
| 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".