Treatment of physical disorder in children with mental disorder: A health care utilization study
Bibliographic record
Abstract
Objective: This study examines, across physician billing, ambulatory and inpatient/emergency datasets, the health care utilization of individuals under the age of 18 years for physical disorders in relationship to the existence of a physician assigned psychiatric disorder. Methods: A retrospective sample of all visit records from three datasets (physician billing, ambulatory records, and inpatient/emergency records n = 12687710) was constructed for cases (n = 26392) and comparisons (n = 205281). The mean number of visits for physical disorders (excluding psychiatric disorders) was calculated for groups defined as cases and comparisons with and without psychiatric disorders. Results: Among Cases and Comparisons with and without psychiatric disorders, physical disorders are significantly greater for any with psychiatric disorder over the 16 years study period in both physician billing and ambulatory datasets. This result differs in the inpatient/emergency dataset in that cases have about 1/3 the number of admissions for physical diagnoses. Conclusion: It was unexpected that cases with a psychiatric diagnosis in the physician billing dataset had fewer physical disorder related inpatient and emergency admissions. We explore the putative explanations for the observed treatment bias related to physical disorders of children with psychiatric disorders.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".