Evaluating cortico-cancellous ratio using virtual implant planning and its relation with immediate and long-term stability of a dental implant- A CBCT-assisted prospective observational clinical study
Bibliographic record
Abstract
BACKGROUND: Primary and long-term implant stabilities are crucial in predicting the success of dental implants. We aimed to evaluate corticocancellous ratio (CCR) around virtual implant using cone beam computed tomography (CT) and assess its relationship with immediate and long-term stability of the implants placed. MATERIALS AND METHODS: A total of 135 image records of posterior mandibular implant sites planned for dental implant were included in our study. CCR was calculated using CT images and implants were placed after stent preparation. Implant stability was calculated immediately, 4 months later, and 2 years later. RESULTS: Pearson's correlation test showed a significant correlation (P and lt; 0.001) between CCR and implant stability. ANOVA and post-hoc Tukey tests showed a significant difference in implant stability between groups with different CCRs at all follow-up timepoints. No significant difference was found between mean implant stability quotient values for low CCR at 2-year follow-up and high CCR immediately after implant placement. CONCLUSIONS: Implant stability is improved with greater CCR. Cortical bone seems to be crucial factor for immediate and long-term stability of a dental implant. Virtual planning using CT can assess implant stability. Further histological studies are required to confirm the relation between CCR and implant stability. The escalating demand of the implant treatment in the dental practice necessitates measuring the several predictors of procedure success. This study introduces a novel predictor (CCR) around virtual implant for detecting the immediate and long-term stability of a dental implant.
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How this classification was reachedexpand
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".