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Record W4200073527 · doi:10.7759/cureus.20466

Yoga for the Management of Attention-Deficit/Hyperactivity Disorder

2021· article· en· W4200073527 on OpenAlexaff
Luxhman Gunaseelan, Manasvi Vanama, Farwa Abdi, Aljeena Rahat Qureshi, Ayesha Siddiqua, Muhammad Amin Hamid

Bibliographic record

VenueCureus · 2021
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of WaterlooYork UniversityUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineAttention deficit hyperactivity disorderRegimenAttention deficitClinical psychologyQuality of life (healthcare)PsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

Yoga has been shown to play a role in reducing the symptoms associated with the inattentive and hyperactive-impulsive forms of attention-deficit/hyperactivity disorder (ADHD). The medical history and clinical findings for a nine-year-old patient presenting with difficulty paying attention and impulsive speech and actions at home and school are presented. After the diagnosis of combination type ADHD by assessment of DSM-5 criteria, both at home and school and through parent and teacher evaluations using National Institute for Children's Health Quality (NICHQ) Vanderbilt Assessment Scales, the patient initiated a yoga training regimen. Six months after initiating the yoga training regimen, follow-up parent and teacher questionnaires revealed improvement in both the inattentive and hyperactive-impulsive symptoms. Literature sourced from the PubMed database to explore the efficacy of yoga for ADHD was used to support the research hypothesis that a structured yoga training regimen improves the symptoms associated with the inattentive and hyperactive-impulsive forms of ADHD, and thus, yoga is recommended as a management technique for individuals with 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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.041
GPT teacher head0.332
Teacher spread0.291 · 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 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

Citations10
Published2021
Admission routes1
Has abstractyes

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