The effect of surface treatments on the bond strength of polyetheretherketone posts: a systematic review protocol
Bibliographic record
Abstract
<ns3:p>Abstract Background Polyetheretherketone (PEEK) is widely used in the biomedical field due to its outstanding biological and mechanical properties. Originally employed as a temporary abutment in implantology, recent research has expanded its indications for more definitive applications, such as frameworks and dental post and core. This shift requires a thorough assessment of PEEK’s adhesion and mechanical characteristics. However, PEEK’s inert properties and intricate chemistry create difficulties in surface treatment, resulting in reduced surface energy and inadequate adhesion. Various physical and chemical modification techniques, including acid etching (e.g., 98% sulfuric acid), sandblasting with alumina oxide (Al₂O₃), plasma treatment, laser irradiation, silanization, and air abrasion with silica-coated particles, have been proposed to enhance PEEK’s bonding performance. Despite its numerous clinical trials, standardized protocols remain lacking. This systematic review aims to assess the impact of surface treatments on the bonding performance of PEEK posts. Methods A detailed search of the literature will be conducted across several databases including PubMed, Scopus and clinical trial registries. Additional databases such as Cochrane Central, EMBASE, Web of Science and EBSCO will also be included. The search strategy will target controlled randomized studies and non-randomized clinical trials evaluating the impact of surface treatments on PEEK post adhesion strength. The Newcastle-Ottawa Scale (NOS) will be used to assess bias in non-randomized studies, while the Cochrane Risk of Bias (ROB II) tool will be employed for evaluating randomized controlled trials. Data extraction will focus on study design, treatment methods, outcomes and results. This systematic review protocol will adhere to the guidelines for systematic reviews outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Discussion The discussion will explore the implications of findings on clinical practice, highlighting the importance of enhancing PEEK’s bioactivity and surface energy to improve bonding efficacy in dental procedures. Moreover, it will suggest areas for future research to advance dental materials science, aiming to optimize the utilization of PEEK in dental applications Systematic review registration PROSPERO: CRD42024529783 (Registered on 08/04/2024).</ns3:p>
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".