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Record W3167427197 · doi:10.2147/ijgm.s297462

Etiological Factors of the Midline Diastema in Children: A Systematic Review

2021· review· en· W3167427197 on OpenAlexaboutno aff
Sivakumar Nuvvula, Sravani Ega, Sreekanth Kumar Mallineni, Basim Almulhim, Abdullah Alassaf, Sara Alghamdi, Yong Chen, Sami Aldhuwayhi

Bibliographic record

VenueInternational Journal of General Medicine · 2021
Typereview
Languageen
FieldHealth Professions
TopicOral and Craniofacial Lesions
Canadian institutionsnot available
FundersMajmaah University
KeywordsMedicineEtiologyDiastemaBioinformaticsDentistryPathologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Midline diastema in children is a prevalent developmental entity, and this pathological condition may remain in many children due to various factors. Nonetheless, the evidence on etiological factors of the midline diastema in children is minimal. PURPOSE: To evaluate the etiological factors of midline diastema causes in children below 12 years of age from the published data. METHODS: covering the period from January 1960 to December 2019. Search in Google Scholar, grey literature, and hand search on references were performed to find additional data. Suitable studies were selected based on the predefined inclusion and exclusion criteria. Quality analysis of the chosen studies conducted using the Newcastle-Ottawa Scale (NOS) adapted for cross-sectional studies. RESULTS: Only eight studies were available for final analysis among those four studies from India, two studies from Korea, one study from Brazil, and another study from Canada. The most common etiology for midline diastema was supernumerary teeth followed by morphology labial frenum and nasal airflow condensation. The quality analysis of these studies based on NOS showed one study with unsatisfactory, four studies with satisfactory, and three with good quality. CONCLUSION: Morphology of frenum, pre-maxillary supernumerary teeth, and nasal airflow condensation seem to be the most common causes of midline diastema in children below 12 years.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.138
GPT teacher head0.503
Teacher spread0.365 · 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 designSystematic review
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

Citations21
Published2021
Admission routes1
Has abstractyes

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