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Record W2753573224 · doi:10.1002/mpr.1586

Assessing risk of bias in randomized controlled trials of methylphenidate for children and adolescents with attention deficit hyperactivity disorder (<scp>ADHD</scp>)

2017· article· en· W2753573224 on OpenAlexaff
Raíssa Rodrigues‐Tartari, Walter Swardfager, Giovanni Abrahão Salum, Luís Augusto Rohde, Hugo Cogo‐Moreira

Bibliographic record

VenueInternational Journal of Methods in Psychiatric Research · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health NetworkSunnybrook Health Science Centre
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMethylphenidateAttention deficit hyperactivity disorderRandomized controlled trialPsychologyAttention deficitAttention deficit disorderPsychiatryClinical psychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

To test how reliable the tool recommend by Cochrane Collaboration for assessing risk of bias systematic reviews of randomized clinical trials is in the context of methylphenidate for children and adolescents with attention deficit hyperactivity disorder. Confirmatory factor analysis was used to evaluate a unidimensional model for the 7 indicators, applied to 184 Randomized Clinical Trial (RCTs) within a 2015 Cochrane systematic review titled "Methylphenidate for children and adolescents with attention deficit hyperactivity disorder." A unidimensional model resulted in excellent adequacy indices, but only 2 indicators had very high factor loadings and low measurement errors. In terms of content, the 7 indicators showed poor reliability (ω = 0.642); however, the set of indicators was precise in evaluating studies with a high amount of bias risk. The Cochrane model of risk of bias as it is, exhibited good fit indices but the majority of the items were not reliable to adequately capture risk of bias in the context of clinical trials of methylphenidate for ADHD.

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)0.085
metaresearch head score (Gemma)0.127
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0850.127
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.190
GPT teacher head0.552
Teacher spread0.363 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2017
Admission routes1
Has abstractyes

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