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Record W2885502620 · doi:10.1007/s40474-018-0147-0

Melatonin and Comorbidities in Children with Autism Spectrum Disorder

2018· review· en· W2885502620 on OpenAlexafffund
Katia Gagnon, Roger Godbout

Bibliographic record

VenueCurrent Developmental Disorders Reports · 2018
Typereview
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsHôpital Rivière-des-PrairiesUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
FundersFonds de Recherche du Québec - SantéChildren Neurodevelopmental Disorders Network
KeywordsAutism spectrum disorderAutismMelatoninPsychologyMedicinePsychiatryClinical psychologyPediatricsDevelopmental psychologyAudiologyNeuroscience

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Melatonin is used to treat sleep difficulties associated with autism spectrum disorder (ASD). There are growing evidence that melatonin could have an effect on other symptoms than sleep, such as anxiety, depression, pain, and gastrointestinal dysfunctions. Interestingly, these symptoms frequently are found as comorbid conditions in individuals with ASD. We aimed to highlight the potential effect of melatonin on these symptoms. RECENT FINDINGS: Animal and human studies show that melatonin reduces anxiety. Regarding the effect of melatonin on pain, animal studies are promising, but results remain heterogeneous in humans. Both animal and human studies have found that melatonin can have a positive effect on gastrointestinal dysfunction. SUMMARY: Melatonin has the potential to act on a wide variety of symptoms associated with ASD. However, other than sleep difficulties, no studies exist on melatonin as a treatment for ASD comorbid conditions. Such investigations should be on the research agenda because melatonin could improve a multitude of ASD comorbidities and, consequently, improve well-being.

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)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.042
GPT teacher head0.323
Teacher spread0.281 · 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
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

Citations52
Published2018
Admission routes2
Has abstractyes

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