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Record W1898480318

Development and testing of fiber-reinforced composite space maintainers.

2010· article· en· W1898480318 on OpenAlexaff
Gajanan Kulkarni, Domenic Lau, Sara Hafezi

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFlexural strengthMaterials scienceComposite materialComposite numberFiberMolarDentistryMedicine
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to develop a clinically acceptable, cheaper, and more expedient alternative to standard stainless steel band and loop space maintainers. METHODS: Loops of fiber-reinforced composites were constructed using polyethylene fiber (Ribbond) and glass fiber (Sticktech). The loops were bonded on extracted third molars and tested for flexural strength before and after thermocycling and following repair of the appliances after initial stress failure. Bacterial colonization on the appliances was also compared. Conventional stainless steel band and loop space maintainers cemented with Ketac were controls. RESULTS: Ribbond samples demonstrated higher flexural strength than Sticktech and the control (P<.05). No differences were noted among the other samples and the control. The repaired Ribbond samples were statistically comparable in flexural strength to the initial samples. Thermocycling resulted in decreased flexural strength of both Ribbond and Sticktech (P<.05). Thermocycled Ribbond samples were comparable to the control, but a lower flexural strength was noted for Sticktech samples (P<.05). While all space maintainers allowed some bacterial adhesion, Sticktech showed higher Streptococcus mutans counts than Ribbond (P=.06). CONCLUSIONS: Ribbond space-maintainers are comparable to the stainless steel in terms of physical strength and biofilm formation. The fiber-reinforced composite space maintainers may be a clinically acceptable and expedient alternative to the conventional band-loop appliance.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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.

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

Citations25
Published2010
Admission routes1
Has abstractyes

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