Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This thesis research investigated the impact of delays in periodontal maintenance appointments \nduring the COVID-19 pandemic on clinical periodontal outcomes (PD, BOP, PI) in a sample size \nof 350 patients who either received (n=260) or had never received (n=90) sanative therapy. A \nhierarchical multiple regression including 3 models was used to evaluate the effect of various \npredictors on post-pandemic clinical outcomes in both groups for a total of 6 regression analyses. \nThe predictors of interest were a disruption due to COVID-19 (Model 1, 2, and 3), length of \ndelay (Model 2 and 3), pre-COVID-19 clinical measures (Model 2 and 3), sex (Model 3), and \nsmoking status (Model 3). The findings showed that a delay in appointment – regardless of \nduration – predicted a worsened PD in patients who have received ST (Model 1). Moreover, a \nlonger delay and poorer pre-COVID-19 clinical measures predicted a worsening of all outcomes \nin patients who have received ST (Model 2). These factors also predicted a greater PI in \nindividuals who have never received ST (Model 2). Smoking status and sex in combination \ninfluenced all outcomes for patients who have received ST, wherein current smokers and female \nsex were linked to a worsening of PD (Model 3). PI was the only clinical outcome significantly \naffected by smoking status and sex in patients who have never received ST (Model 3). Results of \nthis study suggest that patients who have received sanative therapy to treat periodontal disease \nare 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 \nmanaging disruptions in periodontal maintenance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it