Effect of Tea on Gingivits: A Community-based Study
Bibliographic record
Abstract
Pharmacological values of tea have been found by medical researchers in many countries. Catechin compounds found in tea impart several health benefits including mitigating dental ailments. However, whether dento-clinical properties of catechins are camouflaged by additive like milk or not has remained unexplored. A survey-based study involving community people suffering from gum disease (gingivitis) has been carried out to understand whether hot water extract of tea ( known as brew) without milk has any relevance in reducing gingivitis. The survey reveals a strong negative linear correlation ( r= -0.807** ) between drinking tea without milk and reduction of gingivitis. The results also reveal that frequency of gingivitis amongst people who drink tea with milk is more ( r= 0.696* ) as compared to the people who drink tea without milk. Tea made from Tinali 17/1/54 cultivar of Camelia sinensis L. O. Kuntze var, Assamica has been used in the study as this cultivar is ubiquitous in tea growing areas and can produce different categories of black tea – strong CTC tea, mild flavored orthodox tea and milder green tea.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".