Sensitivity and Specificity of Stability Criteria for Immediately Loaded Splinted Maxillary Implants
Bibliographic record
Abstract
BACKGROUND: To assess the suitability of dental implants for immediate loading, primary stability is usually evaluated intraoperatively. PURPOSE: This retrospective study aimed to assess the suitability of three stability parameters - namely, insertion torque (IT), implant stability quotient (ISQ; measured by resonance frequency analysis), and Periotest (PT) values - as potential predictors for the risk of nonosseointegration of immediately loaded splinted implants. The stability parameters were routinely collected under immediate loading. MATERIALS AND METHODS: Nineteen patients with 11 edentulous and 8 partially edentulous maxillae were treated with 105 dental implants, which were immediately loaded using temporary fixed dentures. The IT results, PT values, and ISQ results were recorded. Receiver operating characteristic analysis was performed to assess the quality of each parameter as a diagnostic test. RESULTS: After a 3-month observation period, 11 implants in four patients were not osseointegrated. The IT and ISQ (IT 25.0 ± 12.5 Ncm and 8.4 ± 2.3 Ncm; PT -1.5 ± 3.0 and +2.7 ± 3.0; and ISQ 62.6 ± 6.7 and 54.7 ± 6.2) differed significantly between the osseointegrated and failed implants (p < .005). The IT showed the greatest specificity at a sensitivity of 1 and the greatest area under the curve (AUC; 0.929), followed by the PT value (AUC = 0.836) and ISQ (AUC = 0.811). CONCLUSIONS: Among the intraoperative parameters analyzed, IT showed the highest specificity at a high sensitivity of 1. Therefore, the IT can be considered the most valid prognostic factor for osseointegration of immediately loaded splinted dental implants.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".