Clinical Periodontal Outcomes and COVID-19
Bibliographic record
Abstract
This thesis research investigated the impact of delays in periodontal maintenance appointments during the COVID-19 pandemic on clinical periodontal outcomes (PD, BOP, PI) in a sample size of 350 patients who either received (n=260) or had never received (n=90) sanative therapy. A hierarchical multiple regression including 3 models was used to evaluate the effect of various predictors on post-pandemic clinical outcomes in both groups for a total of 6 regression analyses. The predictors of interest were a disruption due to COVID-19 (Model 1, 2, and 3), length of delay (Model 2 and 3), pre-COVID-19 clinical measures (Model 2 and 3), sex (Model 3), and smoking status (Model 3). The findings showed that a delay in appointment – regardless of duration – predicted a worsened PD in patients who have received ST (Model 1). Moreover, a longer delay and poorer pre-COVID-19 clinical measures predicted a worsening of all outcomes in patients who have received ST (Model 2). These factors also predicted a greater PI in individuals who have never received ST (Model 2). Smoking status and sex in combination influenced all outcomes for patients who have received ST, wherein current smokers and female sex were linked to a worsening of PD (Model 3). PI was the only clinical outcome significantly affected by smoking status and sex in patients who have never received ST (Model 3). Results of this study suggest that patients who have received sanative therapy to treat periodontal disease are more clinically fragile than patients who have never received sanative therapy. Practically, these findings extend beyond the pandemic, offering insights into patient care strategies for managing disruptions in periodontal maintenance.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".