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Record W2092563301 · doi:10.1016/s0924-9338(00)94148-8

S25.03 The behavioural psychopathology of blood disorders. A cross-national study

2000· article· en· W2092563301 on OpenAlexaboutno aff
I. Kolvin, H. Sadowski, Carmen Clemente, Bruce Taylor, C.A. Lee, John Tsiantis, S. Baharaki, M. Mannuci, G. Ba

Bibliographic record

VenueEuropean Psychiatry · 2000
Typearticle
Languageen
FieldMedicine
TopicUrticaria and Related Conditions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychopathologyAction (physics)PsychologyContent (measure theory)Clinical psychologyMathematics

Abstract

fetched live from OpenAlex

S26. Metabolism of amino-acids and synthesis of 8-carbolines in relation to psychopathology 265s Studies to date suggest that offenders with these disorders present multiple difficulties and that in order to prevent further offending it is necessary to specifically address each of these difficulties. The present study was designed to: (1) identify the components of treatment, social services, and laws that effectively prevent crime among persons with major mental disorders; (2) to verify if different types of patients require different treatment programmes; (3) to measure the impact of varying legal powers of clinicians to enforce compliance with treatment; (4) to assess the predictive validity of the HCR-20 in determining the risk of violence; and (5) to assess the validity of hair analysis for measuring medication use and alcohol and drug consumption. In each of four sites, Canada, Finland, Germany and Sweden, two samples of patients with major mental disorders are recruited, one with and one without an official record of crime. As they enter the study, detailed historical information is collected from files and collaterals and they are intensively examined. During the next two years, they are repeatedly examined, collaterals are questioned, all the treatments and services that they receive are documented as well as criminal activities and aggressive behaviours. S25.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.292
Teacher spread0.278 · 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.

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
Published2000
Admission routes1
Has abstractyes

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