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Record W2763885210 · doi:10.1111/jir.12433

Medication use by adolescents and adults with fragile X syndrome

2017· article· en· W2763885210 on OpenAlexaboutno aff
Daniel J. Laxman, Jan S. Greenberg, Leann Smith DaWalt, Jinkuk Hong, M. G. Aman, Marsha R. Mailick

Bibliographic record

VenueJournal of Intellectual Disability Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human Development
KeywordsPsychotropic medicationMedical prescriptionPsychiatryFragile X syndromeMedicineAutismMental healthOddsQuarter (Canadian coin)PediatricsLogistic regression

Abstract

fetched live from OpenAlex

BACKGROUND: The behavioural challenges and medical conditions associated with fragile X syndrome (FXS) can lead to increased need for medications. METHOD: This longitudinal study examined the use of prescription medications for psychotropic and non-psychotropic purposes by adolescents and adults with FXS drawn from a North American community sample (N = 105). Odds and probabilities of continuing or discontinuing medication were calculated. Predictors of medication use were calculated. RESULTS: More than two-thirds took psychotropic medication, and about one-quarter took non-psychotropic medication. Over a 3-year period, those who initially took prescription medications were considerably more likely to remain on medications than to stop. Individuals with more autism symptoms, more behavioural problems, a mental health diagnosis, and greater family income were significantly more likely to use psychotropic medication 3 years later. Individuals who had more health problems, a mental health diagnosis, and were female were more likely to use non-psychotropic medication over this time period. CONCLUSIONS: Findings highlight the elevated and ongoing use of medication by individuals with FXS. Implications for social and behavioural research on FXS are discussed.

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)0.002
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 score0.386
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.036
GPT teacher head0.326
Teacher spread0.290 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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