Comparing Two Diagnostic Procedures in Planning Dental Implants to Support a Mandibular Free‐Ending Removable Partial Denture
Bibliographic record
Abstract
BACKGROUND: The use of a cone beam computed tomography (CBCT) for the preoperative implant planning is increasing. A clear guideline is needed in which cases of CBCT is essential. PURPOSE: In this study, two imaging modalities (panoramic radiograph and CBCT) are compared in preoperative implant planning in the severely resorbed mandible and the influence on the observers assessments. MATERIALS AND METHODS: Thirty-four consecutive patients with bilateral edentulous regions in the mandible were included. The feasibility of implant placement in the premolar and molar region was judged by three observers on basis of casts either with a panoramic radiograph or a CBCT.Cohen's kappa, sensitivity and specificity rates, odds of agreement and disagreement as well as the odds ratios (ORs, ratio between odds of agreement and disagreement) were calculated per observer and overall for all observers assuming the majorities agreement as the prevailing opinion. RESULTS: Overall outcome for premolar region revealed true-positive and true-negative rates of 90% and 0%, respectively, with Cohen's kappa (κ) = -0.04. The ORs for the three observers varied between 2.6 and 158.8, with an overall OR = 76.For the molar region, overall true-positive and true-negative rates were 65% and 22% respectively, with Cohen's κ = 0.68, representing a reasonable amount of agreement. Sensitivity and specificity as well as the ORs for individual observers were fairly consistent with an overall OR = 43. CONCLUSION: Implant placement in the resorbed posterior mandible can be well assessed with a cast in combination with a panoramic radiograph in the vast majority of the cases. Misclassification amounts to approximately 10% to 13%. In all cases of misclassification, a critical bone height, or an unclear course of the mandibular nerve or a knife edge ridge was present. In these cases, the use of a CBCT is justified.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".