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Predictive factors of vertical bone depth in the paramedian palate of adolescents.

2006· article· en· W2176113777 on OpenAlexaff
Keith S. King, Ernest W.N. Lam, M.G. Faulkner, Giseon Heo, Paul W. Major

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of AlbertaUniversity of TorontoMedicine Hat College
Fundersnot available
KeywordsMedicineDentistryUnivariate analysisCone beam computed tomographyOrthodonticsUnivariateMultivariate analysisComputed tomographyMultivariate statisticsSurgeryMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether a relationship exists between the vertical bone depth in the paramedian palate (PP) of growing patients and age, gender, and palatal morphology. Clinically detectable traits may decrease the need for further imaging prior to implant placement for orthodontic anchorage. MATERIALS AND METHODS: Cone beam computed tomagraphic scans (Newtom-9000, Verona, Italy) were acquired in 183 orthodontic patients (10-19 years old). Vertical bone depth was measured at nine unilateral locations in the PP of each subject. Measurements were analyzed with univariate and multivariate statistical tests. RESULTS: Significant variability in the bone thickness was found among locations and among subjects. Male subjects had significantly greater mean bone thickness in six of the nine locations measured, showing a mean of 1.22 mm more vertical bone than females showed at these locations. Age and palatal measurements did not demonstrate a clinically useful relationship with bone depth. CONCLUSIONS: Age and palatal morphology are not valid predictors of bone height in the PP. Because of the large variability of bone thickness in this region, computed tomographic imaging remains valuable prior to paramedian implant placement in growing individuals. The paramedian palate presents a promising region for palatal implant placements when the midpalatal suture is to be avoided.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.324

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 head0.022
GPT teacher head0.238
Teacher spread0.216 · 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

Citations32
Published2006
Admission routes1
Has abstractyes

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