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Record W4319826578 · doi:10.1111/cid.13187

Effect of material type, torque value, and sterilization on linear displacements of a scan body: An in vitro study

2023· article· en· W4319826578 on OpenAlexvenueno aff
Emre Diker, Hakan Terzioğlu, Diogo Gouveia, Mustafa Borga Dönmez, Jeremy D. Seidt, Burak Yılmaz

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2023
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsTorqueMaterials scienceSterilization (economics)Wilcoxon signed-rank testAutoclavePeekComposite materialMaterials testingBiomedical engineeringMathematicsMann–Whitney U testMedicinePhysicsStatistics

Abstract

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BACKGROUND: There is limited knowledge on the effect of scan body (SB) material type, torque value, and sterilization on linear displacements of implant SBs. PURPOSE: To evaluate the effect of material type, torque value, and sterilization on linear displacements of SBs during screw tightening by using digital image correlation (DIC) analysis. MATERIALS AND METHODS: One polyetheretherketone (PEEK, Zfx Intraoral Scan Body) and one titanium SB (Ti, MPS Zimmer Scanbody R1410) were tightened with 5 Ncm torque on two implants (Zimmer TSV ⌀4.7 mm) by using a digital torque limiting device. SBs' initial spatial positions relative to the implants were recorded by using 3D DIC technique. Measurements were repeated after initially increasing torque value to 10 Ncm and then to 15 Ncm, and these steps were repeated for a total of 10 PEEK and 10 Ti SBs on both implants (n = 20). All SBs were then sterilized 25 times by using an autoclave (STATIM 5000 S G4) according to manufacturer's recommendations and all measurements were repeated. Linear displacements on three axes were calculated for each SB with increasing torque values (from 5 to 10 Ncm and from 10 to 15 Ncm) before and after sterilization. SB displacements within each torque value-sterilization pair were compared by using Mann-Whitney U test, whereas Wilcoxon signed-rank test was used to compare SB displacements within each material-torque value pair between conditions and within each material-sterilization pair between torque values (α = 0.05). RESULTS: On x-axis, PEEK SBs had higher displacements than Ti SBs (p < 0.001), whereas sterilization (p ≤ 0.028) and 15 Ncm torque application (p ≤ 0.006) led to higher displacements of PEEK SBs. On y-axis, PEEK SBs had higher displacements than Ti SBs with 15 Ncm torque application (p ≤ 0.033). A total of 15 Ncm torque-applied PEEK SBs and 10 Ncm torque-applied Ti SBs had higher displacements after sterilization (p ≤ 0.028). Application of 15 Ncm torque led to higher displacements regardless of the material (p ≤ 0.002). On z-axis, PEEK SBs had higher displacements (p ≤ 0.015), except for 10 Ncm torque-applied sterilized SBs (p = 0.102). With 10 Ncm torque application, sterilization decreased the displacement values of PEEK SBs (p = 0.044). Greater displacements were observed with 10 Ncm torque-applied Ti SBs before sterilization and 15 Ncm torque-applied PEEK SBs after sterilization (p ≤ 0.033). CONCLUSIONS: Axial displacement of SBs was affected by material type, torque value, and sterilization. Ti SBs mostly had lower displacements than PEEK SBs. Application of 15 Ncm torque to tested PEEK SBs should be refrained from and a calibrated tightening tool may enable the application of 10 Ncm or lower torque values for lower displacements. Sterilization generally increased PEEK SB displacements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.094
GPT teacher head0.511
Teacher spread0.417 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2023
Admission routes1
Has abstractyes

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