The behavior of polyetheretherketone healing abutments when measuring implant stability with electronic percussive testing
Bibliographic record
Abstract
BACKGROUND: It is unknown whether it is possible to measure implant stability with polyetheretherketone (PEEK) healing abutments using electronic percussive testing (EPT). PURPOSE: To investigate the reliability of the percussive test values (PTVs) measured with PEEK healing abutments and to compare them with the PTVs measured with titanium healing abutments. MATERIAL AND METHODS: Thirty dental implants were inserted into the fresh pelvis belonging to a cow. Titanium healing abutments (2 and 5 mm), PEEK healing abutments (5 mm), and prepable standard titanium abutments (5 mm) were screwed to the implants, respectively, and PTVs were measured by two examiners using a wireless EPT device. Differences in PTVs between different dental implant components were evaluated using Friedman's test with post hoc Wilcoxon signed-rank test and Bonferroni correction. Inter and intra-observer reliabilities were detected using interclass correlation coefficients (ICCs) RESULTS: The mean PTVs obtained using the PEEK healing abutments were significantly higher than the mean PTVs obtained using the other abutments for both examiners (P < .01). The ICCs for intra-observer reliability were detected as poor for PEEK healing abutments; and excellent for the other abutment types for both examiners. The ICCs for the inter-observer reliability between the two examiners were poor (0.25) for PEEK healing abutments, and excellent for the 2 and 5 mm titanium healing abutments and for standard abutments (0.82, 0.84 and 0.93, respectively). CONCLUSION: Within the limitations of this in vitro study, it may be concluded that EPT measurements should be avoided using PEEK healing abutments because of the poor reliability.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".