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Record W2129295997 · doi:10.1902/jop.2003.74.7.1032

Scanning Electron Microscope Evaluation of Two Methods of Resharpening Periodontal Curets: A Comparative Study

2003· article· en· W2129295997 on OpenAlexaff
Ofer Moses, Haim Tal, Zvi Artzi, Alon Sperling, Ron Zohar, Carlos E. Nemcovsky

Bibliographic record

VenueJournal of Periodontology · 2003
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBevelScanning electron microscopeEnhanced Data Rates for GSM EvolutionDentistrySharpeningAnalysis of varianceMathematicsOrthodonticsMaterials scienceBiomedical engineeringMedicineComputer scienceComposite materialArtificial intelligenceStatisticsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

BACKGROUND: Effective root planing demands sharp cutting edges on dental curets. However, after several strokes, they become dull and must be resharpened frequently. The purpose of this study was to evaluate by scanning electron microscopy (SEM) the quality of the cutting edge of periodontal curets resharpened by 2 different methods. METHODS: Forty new detachable Gracey curets were used in this study. After similar blunting, all instruments were resharpened either with 10 strokes using an Arkansas fine-grit sharpening stone (AR), or with 7 strokes using a high-grit and -density aluminum oxide stone (CH). The cutting edges of each instrument were examined using SEM at 1 mm and 2 mm from the tip before and after the resharpening procedure. Bevel measurement and the amount of functional and non-functional wire edges (WE) on the cutting edge were evaluated. Data were statistically analyzed using analysis of variance (ANOVA) with repeated measures, 2-way ANOVA, and Fisher's exact test. RESULTS: After blunting and resharpening, differences in bevel between groups were statistically non-significant. Generally, after resharpening, there were significantly more functional and non-functional WE in the AR group than in the CH group. There were significantly more instruments with a complete absence of WE in the CH group. CONCLUSIONS: The CH stone resulted in a smoother and better cutting edge than the AR stone. The procedure was easy to perform and required fewer strokes of the curet on the stone.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.050
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0000.000
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 head0.116
GPT teacher head0.496
Teacher spread0.379 · 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

Citations13
Published2003
Admission routes1
Has abstractyes

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