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Influence of repeated screw tightening on bacterial leakage along the implant–abutment interface

2009· article· en· W2025676714 on OpenAlexaff
Cássio do Nascimento, Vinícius Pedrazzi, Paola Kirsten Miani, Larissa Daher Moreira, Rubens Ferreira de Albuquerque

Bibliographic record

VenueClinical Oral Implants Research · 2009
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsAbutmentDentistryImplantLeakage (economics)Materials sciencePenetration (warfare)Dental AbutmentsBiomedical engineeringMedicineSurgeryStructural engineeringMathematicsEngineering

Abstract

fetched live from OpenAlex

OBJECTIVES: Bacterial penetration along the implant-abutment interface as a consequence of abutment screw loosening has been reported in a number of recent studies. The aim of this in vitro study was to investigate the influence of repeated tightening of the abutment screw on leakage of Streptococcus mutans along the interface between implants and pre-machined abutments. MATERIALS AND METHODS: Twenty pre-machined abutments with a plastic sleeve were used. The abutment screws were tightened to 32 N cm in group 1 (n=10 - control) and to 32 N cm, loosened and re-tightened with the same torque twice in group 2 (n=10). The assemblies were completely immersed in 5 ml of Tryptic Soy Broth medium inoculated with S. mutans and incubated for 14 days. After this period, contamination of the implant internal threaded chamber was evaluated using the DNA Checkerboard method. RESULTS: Microorganisms were found on the internal surfaces of both groups evaluated. However, bacterial counts in group 2 were significantly higher than that in the control group (P<0.05). CONCLUSION: These results suggest that bacterial leakage between implants and abutments occurs even under unloaded conditions and at a higher intensity when the abutment screw is tightened and loosened repeatedly.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.208
GPT teacher head0.525
Teacher spread0.317 · 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 teacher head, not a consensus.

Study designObservational
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

Citations62
Published2009
Admission routes1
Has abstractyes

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