Effect of Eating Cheese on Ca and P Concentrations of Whole Mouth Saliva and Plaque
Bibliographic record
Abstract
A study was undertaken to examine the release of calcium and phosphate from cheese during mastication. Unstimulated saliva was collected for baseline analysis in the initial study followed by saliva collection after chewing different cheeses with and without biscuits. In the second study, volunteers who had abstained from tooth cleaning for 24 h had plaque samples taken from two quadrants, they then chewed cheese in their own personal eating manner, and a second sample of plaque was taken within 5 min. The results showed that the calcium ion concentration of the oral fluids rose from a mean of 30 micrograms/ml to between 200 and 540 micrograms/ml, depending on the type of cheese, but the phosphate concentration fell below baseline. The release of both ions tended to be less when the cheese was eaten with a biscuit. In the second study a highly significant rise in plaque calcium concentration was shown after eating cheese, but no consistent change in phosphate level was found. Acidic soft drinks, following eating, tended to reduce the plaque calcium levels, but no consistent change was found if tea or coffee was taken following the cheese consumption. It is suggested, from these findings, that cheese eaten alone at the very end of a meal raises plaque calcium and might be effective in reducing dental caries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".