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Record W2948223886 · doi:10.1097/adm.0000000000000438

A Multicountry Updated Assessment of the Economic Impact of Fetal Alcohol Spectrum Disorder: Costs for Children and Adults

2018· review· en· W2948223886 on OpenAlexaboutno aff
Jacob R. Greenmyer, Marilyn G. Klug, Cassondra Kambeitz, Svetlana Popova, Larry Burd

Bibliographic record

VenueJournal of Addiction Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsFetal Alcohol Spectrum DisorderMedicineFetal alcoholEnvironmental healthPrenatal alcohol exposureAlcoholPsychiatryPediatricsPregnancy

Abstract

fetched live from OpenAlex

AIM: To conduct a systematic review and quantitative analysis of the world literature on the economic impact of fetal alcohol spectrum disorder (FASD). METHODS: A comprehensive literature review was conducted using multiple electronic databases and reference materials. RESULTS: Thirty-two studies from 4 countries met the inclusion criteria (United States [n = 20], Canada [n = 9], Sweden [n = 2], and New Zealand [n = 1]). The studies reported the economic impact of FASD on health care, special education, residential care, criminal justice system, productivity losses due to morbidity and premature mortality, productivity losses of caregivers of children with FASD, and intangible costs. The economic estimates vary considerably due to the different methodologies used by different studies. The mean annual cost for children with FASD was estimated to be $22,810 and for adults $24,308. Residential costs for children with FASD were 4-fold greater than for adults with FASD. The costs of lost productivity for adults were 6.3-fold greater than for children. CONCLUSIONS: The data on the economic burden of FASD are scarce, and the existing estimates likely underestimate the full economic impact of this disorder on the affected individuals, their caregivers, and society. However, the current research is sufficient to demonstrate that FASD is a serious public health problem associated with tremendous economic burden.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.899
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.341
Teacher spread0.330 · 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 designOther design
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

Citations105
Published2018
Admission routes1
Has abstractyes

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