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Record W4220857421 · doi:10.1111/1556-4029.15015

Crown dimensions of primary teeth—A systematic review and meta‐analysis

2022· review· en· W4220857421 on OpenAlexaboutno aff
P Sujitha, R Bhavyaa, MS Muthu, Latha Nirmal, Sneha Patil

Bibliographic record

VenueJournal of Forensic Sciences · 2022
Typereview
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsCrown (dentistry)MolarDentistryOrthodonticsMedicineMeta-analysisPermanent teeth

Abstract

fetched live from OpenAlex

Abstract Odontometrics, also known as metric traits, includes mesiodistal, buccolingual dimensions, and crown height. The purpose of this study was to assess pancontinental odontometric variations in the crown dimensions of primary teeth. Ten electronic databases were searched to identify studies that measured crown dimensions of primary teeth, published in English language, without year restriction up to July 2020. Studies included cross‐sectional research measuring on casts, subjects, and on radiographs of healthy children. Meta‐analysis was performed, and risk of bias was assessed using modified Newcastle–Ottawa Scale. Eighty‐seven observational studies were included, with 24,634 participants (9487 males, 11,083 females; 19 studies lacked gender information). Only one study showed a low bias risk, whereas 81 and 5 studies had moderate and high risk, respectively. Sixty‐five studies included for meta‐analysis revealed heterogeneity in mean mesiodistal dimensions of maxillary first molars from Asia ( I 2 —99.7%), buccolingual measurements of mandibular first molars from Europe ( I 2 —99.9%), crown height of mandibular second molars from Africa and Europe ( I 2 —79.8%). Among mesiodistal and buccolingual dimensions, Australians have larger while Asians have smaller teeth. Pertaining to crown height, very few studies could be found in the literature. This review highlights the variations in crown dimensions of primary teeth among populations.

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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.088
GPT teacher head0.358
Teacher spread0.270 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations14
Published2022
Admission routes1
Has abstractyes

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