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Record W4296344684 · doi:10.1055/s-0042-1749360

Are Molar-Incisor Hypomineralization and Hypomineralized Second Primary Molars Predictive of Dental Caries?: A Systematic Review

2022· review· en· W4296344684 on OpenAlexaboutno aff
M. Ben Salem, Farah Chouchène, Fatma Masmoudi, Ahlem Baaziz, Fethi Maatouk, Hichem Ghédira

Bibliographic record

VenueEuropean Journal of Dentistry · 2022
Typereview
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMolarDentistryIncisorMedicineOrthodontics

Abstract

fetched live from OpenAlex

Abstract To estimate the prevalence of dental caries among children affected concomitantly with molar-incisor hypomineralization (MIH) and hypomineralized second primary molars (HSPM). Electronic databases, including Medline via PubMed, Cochrane Library, Scopus, and Science Direct, were searched. Studies written in English involving children with MIH-HSPM and dental caries were considered eligible. Two reviewers extracted the data according to the PRISMA statement and assessed the bias risk with the Newcastle–Ottawa Scale (NOS) criteria. Out of 535 studies identified in the initial research, only two articles were included in the present review. A significant association between MIH-HSPM and dental caries in permanent dentition was reported in the two included records (AOR: 3.70; p < 0.001). Children with MIH-HSPM expressed significantly higher DMFT and PUFA values than children without MIH-HSPM. However, dmft values did not differ significantly between children with and without HSPM (1.79 [2.4] vs. 1.78 [2.5]; 1.88 [2.73] vs. 1.59 [2.53], respectively. MIH-HSPM lesions were correlated with a higher prevalence and more severe carious lesions in permanent dentition. However, the results should be interpreted cautiously because the findings were generated by only two studies performed by the same authors. Thus, further well-designed studies are highly recommended.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.300
Teacher spread0.254 · 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.

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

Citations19
Published2022
Admission routes1
Has abstractyes

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