Radiographic changes of trabecular bone density after loading of implant‐supported complete dentures: A 3‐year prospective study
Bibliographic record
Abstract
BACKGROUND: Bone tissues may undergo remodeling under functional mechanical stimuli. PURPOSE: This prospective study on implant-supported fixed complete dentures (IFCDs) evaluated the radiographic trabecular bone changes in density by means of gray levels and texture analysis variables after up to 3-year loading. MATERIALS AND METHODS: The sample consisted of digital periapical radiographs of 63 distal implants of hybrid IFCDs installed in 30 patients (22 women, mean age of 62 ± 7.8 years). Digital periapical radiographs were taken after prosthesis installation, and 1 and 3 years after IFCD loading. Longitudinal images of each implant were superimposed, and the same regions of interest were selected for measurement of gray levels statistics (mean gray levels, SD, and coefficient of variation [CV]) and texture parameters (correlation, contrast, entropy, and angular second moment). Data were analyzed by mixed regression models. RESULTS: Mean gray levels increased for 1 year (P < .05), for 3 years (P < .01) and for maximum bite force (P < .01). The interaction between bruxism and time in 1 year was significant (P < .01) for a decrease in CV. No significant effect of texture analysis variables was found (P > .05). CONCLUSIONS: The results suggest an increase of radiographic bone density as measured by an increase in mean gray levels and a decrease in CV in IFCD distal implants up to 3 years of loading.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".