Stability/change of DSM diagnoses among children and adolescents assessed at a university hospital: a cross-sectional cohort study.
Bibliographic record
Abstract
OBJECTIVES: This study's aim was to examine changes or stability of DSM diagnoses in children and adolescents over the period from childhood to young adulthood and to discuss the instability in DSM diagnoses from a developmental perspective. METHOD: We used cross-sectional cohort design to assess the congruence of DSM diagnoses in children and adolescents who had a diagnostic assessment at least twice as inpatient and/or outpatient at a university hospital from age 5 to 22. Data analysis was conducted using kappa statistics RESULTS: The hospital computerized database consisted of 264 patients who were born from 1983 to 1985 and had had a diagnostic assessment at least twice over a 17-year period. The highest percentages of stable cases were of Mood disorders and Psychosis. Behavioural disorders and Anxiety disorders had lower percentages of stable cases but significant Kappa values suggesting fewer cases were stable but also fewer new cases were added. Substance related disorders had very low percentages and non-significant Kappa value. When divided into three groups based on the delay between first and second diagnosis, stability of diagnosis degraded sharply with time. CONCLUSIONS: The results of this study show poor stability for all diagnoses, however the trend seemed to follow that reported in previous literature where moods disorders and schizophrenia showed more stability than other diagnoses. Explanations are provided for the results. A well-designed prospective longitudinal study utilizing structured diagnostic interviews to assign DSM-IV TR diagnosis from child hood to adulthood would improve the reliability of diagnoses and perhaps time for crystallization of psychopathology and clarification into more discrete diagnostic entities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".