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Record W2132189996

Costs of fetal alcohol spectrum disorder in Alberta, Canada.

2009· article· en· W2132189996 on OpenAlexaffabout
Nguyễn Xuân Thành, E Jonsson

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsFetal Alcohol Spectrum DisorderMedicineEconomic costCohortFetal alcoholTotal costCalculatorAverage costEnvironmental healthDemographyPediatricsPregnancyBusinessEconomics
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Although many programs targeting fetal alcohol spectrum disorder (FASD) are implemented, the province of Alberta is still lacking information on costs of FASD. OBJECTIVES: To estimate the costs of FASD in Alberta based on available US and Canadian research on costs of FASD, and Alberta data. METHODS: Two types of costs were estimated. The annual long-term economic cost of FASD, which referred to a projected amount of money incurred by lives of the cohort of children born with FASD each year, was estimated by multiplying the lifetime cost of caring for each child born with FASD with the number of children born with FASD each year. The annual short-term economic cost of FASD, which referred to the amount of money incurred by people who are presently living with FASD, was estimated by using a FASD cost calculator online at http://www.online-clinic.com. Both were societal costs adjusted to 2008 Canadian dollars. RESULTS: The annual long-term economic cost from the disorders rose from $130 to $400 million each year for the Alberta economy. The annual short-term economic cost for FASD in Alberta was from $48 to $143 million, and the daily cost for FASD in Alberta was from $105 to $316 thousand. CONCLUSION: These numbers suggest a need for a provincial FASD prevention strategy. The costs of FASD can be used to evaluate the benefits of prevention programs to society.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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 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

Citations55
Published2009
Admission routes2
Has abstractyes

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