Autism Associated with B-Vitamin Deficiency Linked to Sugar Intake and Alcohol Consumption
Bibliographic record
Abstract
Objectives: Autism rates in the United States are increasing at a rate of 10-15% per year. This study uses nutritional epidemiology and relates autism rates to the total B-vitamin intakes. The total amounts of B-vitamins are then compared to the previously established minimal daily requirements to see if the intakes are adequate. The apparent lower B-vitamins may result from the increased consumption of sugar and alcohol which are devoid of vitamins, minerals, protein, fat and antioxidants. Study Design: The autism rate was then compared to the percent exclusive per cent breast feeding from 2000-2004 as well as 2007-2010. Other comparisons were made between the statewide exclusive breast feeding and the binge drinking per state. The percentage of infants who participated in Washington state WIC (Womens Infant & Childrens) program were also compared to the autism rate in each county. The autism rate among 8 year olds when compared to the % increase of sugar consumption from 2002 to 2010 also showed an increase in the autism rate. Results: The total amounts of B vitamins in breast milk seemed to be inadequate compared to published mdrs. There was also a direct relationship to the autism rate with the women who were breast feeding from 2000-2004 and 2007-2010. Increased autism rates were related to increased sugar consumption and to an increased alcohol intake. Conclusions: The mothers who are exclusively breast feeding should continue their prenatal vitamins. Sugar intake and alcohol consumption should be decreased. The results suggest that autism is nutritionally related.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".