The Relationship Between Physical and Mental Disorders in a Pediatric Population
Bibliographic record
Abstract
Introduction Few studies examine comorbidity in a pediatric population. This poster presents results that extend our understanding of the relationship between mental disorder and physical disorders using a population-based study approach. Objectives and aims To review the evidence behind comorbidity of psychiatric disorders and other medical disorders. To propose an informatic approach that evaluates those comorbidity on a population-scale. Methods Using an informatics approach, a dataset containing physician billing data for 235,968 (51% male) individuals up to 18 years old spanning sixteen fiscal years (1994–2009) in Calgary, Alberta, was compiled permitting examination of the relationship between physical disorders and mental disorders, based on the International classification of diseases (ICD). Results All major classes of ICD physical disorders had odds ratios with confidence intervals above the value of 1.0, ranging from 1.08 (Perinatal Conditions in 4–6 year olds) to 4.95 (Respiratory Conditions in 0-3 year olds). Distinct major class ICD disorder patterns arise in comparing all children with adults and specific age strata for those under 19 years of age. Conclusions This study represents the first evidence reported in a population-based data set of the effect of mental disorders on each major class of ICD diagnoses related to a physical disorder. The focus on the early intertwinements between physical and mental disorders in a pediatric population may help to target strategic areas for future research and investment. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".