Safety and Efficacy of Community Water Fluoridation
Bibliographic record
Abstract
In 2011, against the beautiful backdrop of the Canadian Rockies and Banff National Park, the drinking water fluoride injection system for the city of Calgary, Canada, needed repair.The estimated cost was $20 million, and to save money, the city council decided to halt water fluoridation.Thirteen years later, they are adding fluoride back to drinking water.During the cessation period in Calgary, hospitals reported a significant rise in children requiring intravenous antibiotics for dental infections, and a study showed that dental caries prevalence increased significantly [1].Despite these consequences, some in the United States (US) are now calling for the removal of fluoride from our drinking water.Community water fluoridation (CWF) is considered to be one of the most important public health interventions of the 20th century [2].It reduces the incidence of tooth decay by approximately 25% in children and adults, and is a safe, passive, and equitable intervention that benefits oral health regardless of age, income, or access to dental care [3,4].In the US, more than 200 million people live in areas served by CWF. Oral Health is Connected to Overall HealthMaintaining oral health is key to overall health.Dental caries is one of the most common diseases that degrades oral health and leads to diminished quality of life for patients.Oral health issues, specifically gum disease and tooth decay, are associated with chronic health conditions, including type 2 diabetes mellitus, cardiovascular disease, cerebrovascular disease, and pulmonary disease [8].
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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 | 0.024 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".