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Record W4415147246 · doi:10.1002/jemt.70083

Comparative Evaluation of Alumina, Hydroxyapatite, and Their Combination for Dental Enamel Cavity Cutting in an Air Abrasion System: An In Vitro <scp>SEM</scp> Study

2025· article· en· W4415147246 on OpenAlexaff
Saqib Ali, Ahmed Talal, Imran Farooq

Bibliographic record

VenueMicroscopy Research and Technique · 2025
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnamel paintAbrasion (mechanical)MolarScanning electron microscopeAbrasiveOral cavity

Abstract

fetched live from OpenAlex

Dental air abrasion is a minimally invasive technique using abrasive particles to remove carious tooth structure. Alumina particles, commonly preferred due to their cutting efficiency, pose toxicity risks if inhaled. This study evaluated hydroxyapatite (HA) and an alumina + HA combination as alternatives to alumina for enamel cutting efficiency. Extracted human third molars with sound enamel (N = 30) were divided into three groups: (1) alumina 29 μm (control), (2) HA, and (3) alumina + HA. Morphological analysis of powders and cavity cutting performance were assessed using scanning electron microscopy (SEM). Alumina particles were coarse and angular, HA particles were rounded, and alumina + HA showed mixed morphology on SEM analysis. Cavity cutting results showed alumina produced the deepest cavities (mean: 2.5 mm), followed by alumina + HA (mean: 2.12 mm) and HA alone (mean: 0.75 mm). Statistically significant differences were detected between alumina and HA (p = 0.0003) and alumina + HA and HA (p = 0.008), but no significant differences between alumina and alumina + HA (p > 0.99) were observed. SEM analysis of the shape of the cavities revealed cylindrical shapes for alumina and alumina + HA groups and conical shapes for the HA group. The alumina + HA combination demonstrated effective enamel cutting efficiency while the presence of HA could be potentially useful for remineralization, presenting a safer alternative to pure alumina. Further in vivo studies are recommended to validate these findings.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.067
GPT teacher head0.439
Teacher spread0.372 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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