Marginal healing using Polyetheretherketone as healing abutments: an experimental study in dogs
Bibliographic record
Abstract
OBJECTIVE: To evaluate the marginal soft and hard tissue healing at titanium and Polyetheretherketone (PEEK) healing implant abutments over a 4-month period. MATERIAL AND METHODS: In six Labrador dogs, all mandibular premolars and first molars were extracted. After 4 months of healing, flaps were elevated, and two implants were installed at each side of the mandible, one in the premolar and the other in the molar regions. Four different types of healing abutments were positioned on the top of each implant: (i) titanium (Ti); (ii) PEEK material bonded to a base made of titanium (Ti-P), randomly positioned in the premolar region; (iii) PEEK, pristine (P); and (iv) PEEK, roughened (P-R), randomly positioned in the molar region. The flaps were sutured to allow a non-submerged healing, and after 4 months, the animals were sacrificed and ground sections obtained for histological evaluation. RESULTS: A higher resorption of the buccal bone crest was observed at the PEEK bonded to a base made of titanium abutments (1.0 ± 0.3 mm) compared to those made of titanium (0.3 ± 0.4 mm). However, similar dimensions of the peri-implant mucosa and similar locations of the soft tissues in relation to the implant shoulder were observed. No statistically significant differences were seen in the outcomes when the pristine PEEK was compared with the roughened PEEK abutments. The mean apical extension of the junctional epithelium did not exceed the implant shoulder at any of the abutment types used. CONCLUSIONS: The coronal level of the hard and soft tissues allows the conclusion that the use of PEEK as healing abutments may be indicated.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".