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Record W2979456290

Clinical Performance of Pedo Jacket Crowns in Maxillary Anterior Primary Teeth.

2016· article· en· W2979456290 on OpenAlexaff
Aimee R. Castro, Sherine Badr, Wafa El‐Badrawy, Gajanan Kulkarni

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDentistryCrown (dentistry)MedicineAnterior teethOral hygieneOrthodonticsMaxillary central incisor
DOInot available

Abstract

fetched live from OpenAlex

PURPOSES: To assess the clinical performance of Pedo Jacket crowns for restoration of carious primary anterior teeth. METHODS: A total of 129 carious primary incisors and canines of 48 children younger than 71 months of age- were restored with Pedo Jacket crowns and resin-modified glass ionomer cementation. They were assessed for: ease of use; presence of recurrent decay; wear; partial or complete loss of the crown; color stability; gingival health; and overall clinical success over a 12-month follow-up. The patient's behavior at the restorative appointment during crown placement was also assessed. RESULTS: An overall clinical success of 89.5 percent of the teeth in 87.3 percent of the children was seen one year later. The crowns were easy to use, even in uncooperative children. The color stability, wear, plaque accumulation, and gingival health were acceptable. Discoloration, wear, or complete loss of the crown were found in 13.1 percent, 5.4 percent, and 7.6 percent of children, respectively. Although not statistically significant, failures were associated with poor patient cooperation at the time of crown placement, poor oral hygiene, or operator error. CONCLUSION: Pedo Jacket crowns are a viable treatment alternative for carious maxillary primary anterior teeth.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0020.001

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 head0.029
GPT teacher head0.273
Teacher spread0.245 · 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 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

Citations8
Published2016
Admission routes1
Has abstractyes

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