Sensitivity and Specificity of Radiographic Methods for Predicting Insertion Torque of Dental Implants
Bibliographic record
Abstract
BACKGROUND: Subjective radiographic classifications of alveolar bone have been proposed and correlated with implant insertion torque (IT). The present diagnostic study aims to identify quantitative bone features influencing IT and to use these findings to develop an objective radiographic classification for predicting IT. METHODS: Demographics, panoramic radiographs (taken at the beginning of dental treatment), and cone-beam computed tomographic scans (taken for implant surgical planning) of 25 patients receiving 31 implants were analyzed. Bone samples retrieved from implant sites were assessed with dual x-ray absorptiometry, microcomputed tomography, and histology. Odds ratio, sensitivity, and specificity of all variables to predict high peak IT were assessed. RESULTS: A ridge cortical thickness >0.75 mm and a normal appearance of the inferior mandibular cortex were the most sensitive variables for predicting high peak IT (87.5% and 75%, respectively). A classification based on the combination of both variables presented high sensitivity (90.9%) and specificity (100%) for predicting IT. CONCLUSIONS: Within the limitations of this study, the results suggest that it is possible to predict IT accurately based on radiographic findings of the patient. This could be useful in the treatment plan of immediate loading cases.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".