Sixteen-year population-based cohort study of main class International Classification of Diseases associated with psychiatric disorders in a sample under the age of two years
Bibliographic record
Abstract
Introduction This paper illustrates the use of cohort data from a population to describe the early life prevalence and odds ratios (ORs) of the main classes of International Classification of Diseases (ICD) associated with any mental disorder arising at any time during the 16 year study period. Objectives The main ICD disorder classes were examined in relation to psychiatric disorders over 16 years in a cohort under the age of two years between April 1st, 1993, and January 1st, 1995. Aims To demonstrate the utility of studying the complete profile of associated diagnoses over time in a population cohort. Methods The total number of individuals under the age of two years before 1995 (n = 17,603) were tallied within each main class of ICD disorder by year and expressed as ORs of those with and without any 16-year psychiatric disorder. Results The greatest annual rates observed in the early years of life were for the following main ICD classes of disease: respiratory system, sense organs, symptoms signs ill-defined conditions, no diagnosis, injury poisoning, and skin subcutaneous tissue disorders. These disorders also had the highest ORs in early life given the presence of a mental disorder at any time during the study period. Discussion Knowing the early life main class diagnoses associated with psychiatric disorders could guide both basic science research as well as early intervention social and health investment policies. 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".