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Record W4214602400 · doi:10.1016/s0924-9338(09)70617-0

MEDED - a Novel Interactive Manual to Enhance Psychopharmacologic Care in Children and Adolescents [PW10-02]

2009· article· en· W4214602400 on OpenAlexaffabout
Stan Kutcher

Bibliographic record

VenueEuropean Psychiatry · 2009
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIntervention (counseling)Presentation (obstetrics)Collaborative CareVariety (cybernetics)Mental healthMedicineHealth careProcess (computing)Medical educationPsychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Child and Adolescent psychopharmacology is a complex yet frequently necessary psychiatric intervention that requires the participation of the patient, the family, other caregivers, other health providers as well as physicians. Provision of up to date information about and education in the use of psychotropics is an essential component of good psychopharmacologic treatment. To date there have been no methods of addressing these needs within a collaborative care framework that meets the interests of all parties and optimizes the application of best available knowledge in the clinical care of child and adoelscents with mental disorders. MEDED (C) was developed by a group of pharmacists and child psychiatrists as an innovative tool to meet this need. It has been extensively evaluated in a variety of clinical settings in Canada with substantial positive results. This presentation will review the novel collaborative approach to pharmacotherapy that MEDED supports, the process of MEDED development, the contents of MEDED and research on its use to date. Clinicians and program directors will have the opportunity to learn how to obtain further information abot the use of this tool.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0410.005

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 head0.009
GPT teacher head0.338
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2009
Admission routes2
Has abstractyes

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