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Record W3213227327 · doi:10.9740/mhc.2021.11.320

Long-term use of antidepressants, mood stabilizers, and antipsychotics in pediatric patients with a focus on appropriate deprescribing

2021· article· en· W3213227327 on OpenAlexaff
Danielle L. Stutzman

Bibliographic record

VenueMental Health Clinician · 2021
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsDeprescribingMedicinePsychiatryPsychotropic AgentPsychotropic medicationMoodPsychotropic drugPsychologyIntensive care medicineMental healthDrugPolypharmacy

Abstract

fetched live from OpenAlex

It is estimated that 8% to 12% of youth are prescribed psychotropic medications. Those in foster care, juvenile justice systems, residential treatment facilities, and with developmental or intellectual disabilities are more likely to be prescribed high-risk regimens. The use of psychotropic medications in this age group is often off-label and can be associated with significant risk, warranting critical evaluation of their role. Landmark trials, pediatric-specific guidelines, and state-driven initiatives play critical roles in supporting evidence-based use of psychotropic medications in children. Overall, there is a lack of literature describing the long-term use of psychotropic medications in youth-particularly with regard to neurobiological, physical, and social changes that occur throughout development. Deprescribing is an important practice in child and adolescent psychiatry, given concerns for over-prescribing, inappropriate polytherapy, and the importance of reevaluating the role of psychotropic medications as children develop.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.060
GPT teacher head0.353
Teacher spread0.293 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

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