Radiographic Periodontal Bone Loss in Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: We examined the extent and severity of radiographic periodontal bone loss in patients with different stages of chronic kidney disease (CKD) and explored a potential dose-response relationship between bone loss and CKD-related biomarkers. METHODS: Panoramic radiographs were obtained from 129 CKD patients (78 males and 51 females; mean age: 63.5 years, range: 24 to 91 years), including 63 patients undergoing dialysis for an average of 3.3 years (range: 0.5 to 14 years). Glomerular filtration rate (GFR), dialysis dose, and levels of serum biomarkers were obtained through a hospital database. Interproximal bone loss was assessed as a percentage of root length. RESULTS: Twenty-nine participants were edentulous (23.8% of those on dialysis versus 21.2% of those with residual kidney function; χ(2) test, P = 0.724). The extent of bone loss was higher among dialysis patients (analysis of variance [ANOVA], P = 0.007), but no clear dose-response association between CKD stage and extent was evident. GFR, dialysis dose, and levels of serum biomarkers did not differ between edentulous and dentate individuals, and only serum albumin was lower in patients with extensive bone loss (ANOVA, P = 0.030). After adjusting for dialysis status, the severity of bone loss was positively associated with glucose levels (multiple regression, P = 0.019) and white blood cell count (P = 0.032), whereas the number of teeth present was positively associated with plasma phosphorus (P = 0.008) and negatively with glucose levels (P = 0.011). CONCLUSION: Despite a higher extent of bone loss in dialysis patients, the lack of a dose-response association between bone loss and CKD stage or the levels of CKD-related serum biomarkers underscores the complex relationship between the two conditions.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".