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Record W2728685799 · doi:10.1016/j.eurpsy.2017.01.420

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

2017· article· en· W2728685799 on OpenAlexaff
David Cawthorpe

Bibliographic record

VenueEuropean Psychiatry · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCohortMedicinePsychiatryPopulationOdds ratioPrevalence of mental disordersCohort studyPediatricsMental healthInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.274
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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