Higher Intakes of Fruits and Vegetables, α‐Linolenic Acid, Vitamin E and β‐carotene are Associated with Improved Periodontal Healing after Periodontal Therapy
Bibliographic record
Abstract
Periodontitis is a chronic inflammatory disease and risk factor for tooth loss. While a link between diet and periodontal health exists, the relationship between diet and healing following periodontal therapy has yet to be investigated. This study determined if higher intakes of foods and nutrients with antioxidant or anti‐inflammatory activity in patients (n=63) with chronic generalized periodontal disease are associated with greater healing – measured as reduced probing depth (PD) ‐ following scaling and root planing (SRP). SRP is a first line cost‐effective treatment to manage periodontal disease and prevent tooth loss. PD was assessed at baseline and between 8 and 16 weeks following SRP. Intakes of fruits, vegetables, β‐carotene, vitamin C, vitamin E, α‐linolenic acid (ALA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) were estimated using the Block 2005 food frequency questionnaire and a supplement use questionnaire. Serum 25‐hydroxyvitamin D concentrations were measured using an automated immunoassay. PD (% sites > 3 mm) was modeled in multiple linear regression analyses and adjusted for age, sex, BMI, baseline PD, examiner and gingival bleeding. PD was associated with fruit and vegetable intake (β = ‐0.29, p = 0.012), dietary β‐carotene intake (β = ‐0.26, p = 0.027), dietary vitamin E intake (β = ‐0.26, p = 0.043), and dietary ALA intake (β = ‐0.24, p = 0.035) but not other intakes or supplements or serum 25‐hydroxyvitamin D. In conclusion, higher intakes of fruits and vegetables, ALA, vitamin E and β‐carotene are associated with reduced PD after SRP and may optimize healing after a periodontal procedure.
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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 | 0.000 | 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".