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Record W3204485456 · doi:10.1111/aej.12571

Palatogingival grooves associated with periodontal bone Loss of maxillary incisors in a Chinese population

2021· article· en· W3204485456 on OpenAlexaff
Rui Zhang, Jie Xiong, Markus Haapasalo, Ya Shen, Liuyan Meng

Bibliographic record

VenueAustralian Endodontic Journal · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOral and gingival health research
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsMedicineDental alveolusMaxillary central incisorDentistryChinese populationOrthodonticsPopulationIncisorMaxillary incisorBiology

Abstract

fetched live from OpenAlex

This study aimed to investigate the prevalence of palatogingival grooves (PGGs) in a Chinese population and the relationship between different types of PGGs and periodontal bone loss. CBCT images of 1715 patients were included in the study. The prevalence of PGGs of the maxillary incisors by sex and tooth type was analysed. The severity of alveolar bone loss in different types of PGGs was assessed. The reasons for taking the CBCT from patients with PGGs were collected. The frequency of PGGs in males (10.16%) was higher than that in females (7.05%) (P < 0.05). PGGs were present more often in maxillary lateral incisors (4.5%) than in maxillary central incisors (0.29%). Compared with other types of PGGs, the type I PGGs were the most prevalent configuration and accompanied with less severity of alveolar bone loss (P < 0.05). Less than half of PGGs cases (47.9%) were prescribed CBCT examination because of the PGGs observed or suspected clinically. The prevalence of PGGs in a Chinese population was higher in males than in females. The different types of PGGs might lead to different severity of periodontal bone destruction. Clinicians should be aware of the presence of PGGs in maxillary incisors, particularly maxillary lateral incisors.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.418
Teacher spread0.361 · 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 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

Citations17
Published2021
Admission routes1
Has abstractyes

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