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Record W2097119161 · doi:10.1177/0269881111412095

Systematic review of national and international guidelines on attention-deficit hyperactivity disorder

2011· review· en· W2097119161 on OpenAlexaff
Miguel de Seixas, Margaret D. Weiss, Ulrich Müller

Bibliographic record

VenueJournal of Psychopharmacology · 2011
Typereview
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGuidelinePsychosocialAttention deficit hyperactivity disorderPsychological interventionPsychiatryMEDLINEGrey literaturePsychologyEvidence-based medicineClinical psychologySystematic reviewMedicineAlternative medicinePolitical science

Abstract

fetched live from OpenAlex

During the last few years several clinical guidelines on attention-deficit hyperactivity disorder (ADHD) have been published by national and international medical societies. To systematically review and compare recommendations of selected ADHD guidelines, we performed a systematic search in online guideline databases and PubMed in order to retrieve guideline texts. Guidelines meeting inclusion criteria were reviewed and recommendations on assessment and treatment extracted. The AGREE instrument was used to assess methodological quality. Of the 26 guidelines identified, 13 were selected for further analysis: 11 guidelines deal with ADHD in childhood and adolescence and 5 guidelines cover transitional patients and/or ADHD in adults. The methodological quality of ADHD guidelines is moderate to good. They reflect similarities and differences of healthcare systems. Diagnosis throughout the lifespan is based on a detailed clinical history. There is greater agreement on evidence-based pharmacological treatment than on psychosocial interventions, reflecting the strength of evidence.

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)0.021
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.094
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0190.018
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.128
GPT teacher head0.476
Teacher spread0.348 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations195
Published2011
Admission routes1
Has abstractyes

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