Association of Child and Adolescent Psychiatric Disorders with Somatic or Biomedical Diagnoses: Do Population-Based Utilization Study Results Support the Adverse Childhood Experiences Study?
Bibliographic record
Abstract
CONTEXT: Few population-based studies have examined the relationship between psychiatric and somatic or biomedical disorders. OBJECTIVE: We examined the effect of the presence or absence of any psychiatric disorder on somatic or biomedical diagnosis disorder costs. Guided by the Kaiser Permanente and Centers for Disease Control and Prevention Adverse Childhood Experiences (ACE) Study, we examined our administrative data to test if psychiatric disorder is associated with a higher level of somatic disorder. DESIGN: A dataset containing registration data for 205,281 patients younger than age 18 years was randomly selected from administrative data based on these patients never having received any specialized, publicly funded ambulatory, emergency or inpatient admission for treatment of a psychiatric disorder. All physician billing records (8,724,714) from the 16 fiscal years April 1993 to March 2009 were collected and grouped on the basis of presence or absence of any International Classification of Diseases (ICD) psychiatric disorder. MAIN OUTCOME MEASURES: We compared 2 groups (with or without any psychiatric disorder: dependent variable) on the cumulative 16-year mean cost for somatic (biomedical, nonpsychiatric) ICD diagnoses (independent variable). RESULTS: Billing costs related to somatic and biomedical disorders (nonpsychiatric costs) were 1.8 times greater for those with psychiatric disorders than for those without psychiatric disorders. Somatic costs peaked before the age of 6 years and remained higher than the groupings without psychiatric disorders in each age range. CONCLUSION: In support of the ACE study, ICD psychiatric disorders (as an index of developmental adversity) are associated with substantially greater ICD somatic disorders. The findings have implications for health care practice.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".