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Record W2164702157

Antidepressants for functional gastrointestinal disorders in children.

2013· article· en· W2164702157 on OpenAlexaff
Edmund Tan, Christine H. Smith, Ran D. Goldman

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

QUESTION: Functional gastrointestinal disorders (FGIDs) are complex conditions I see in some of my pediatric patients. The indications for antidepressants such as selective serotonin reuptake inhibitors (SSRIs) do not include treatment of FGIDs; however, some children are prescribed SSRIs for this condition. Are antidepressants effective and safe to use for treating FGIDs in children and adolescents? ANSWER: The pathogenesis of FGIDs is largely idiopathic, and although theories exist to explain why SSRIs might be used to treat FGIDs, there is no conclusive evidence of their effectiveness. No large, well controlled studies have investigated the use of SSRIs to treat FGIDs in the pediatric population. There is also evidence that suggests an increased risk of suicidal thoughts when adolescents use SSRIs. Currently, there is no recommendation to use SSRIs to treat FGIDs in children.

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)0.006
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0300.010

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 head0.017
GPT teacher head0.213
Teacher spread0.196 · 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
GenreOther

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

Citations2
Published2013
Admission routes1
Has abstractyes

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