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Record W7117455591 · doi:10.17605/osf.io/b9smx

Association of Matrilin-1 (MATN1) and Myosin 1H (MYO1H) Polymorphisms with Mandibular Prognathism

2025· other· W7117455591 on OpenAlexaboutno aff
dr chetan kumar, Shravan vaiyapuri, DR Bharath Rangarajan

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsOdds ratioMandibular prognathismGenotypeConfidence intervalGenetic associationPolymorphism (computer science)Candidate geneMyosin

Abstract

fetched live from OpenAlex

Background: Mandibular prognathism (Class III malocclusion) is a complex skeletal discrepancy with a strong genetic component. Recent genome-wide association studies (GWAS) and candidate gene studies have implicated Matrilin-1 (MATN1), a cartilage matrix protein, and Myosin 1H (MYO1H), a molecular motor protein, as key susceptibility markers. However, results across different ethnic populations remain inconsistent. Objective: To assess the association between specific polymorphisms in MATN1 (e.g., rs533895) and MYO1H (e.g., rs10850110) and the susceptibility to mandibular prognathism. Methods: A systematic search will be conducted using PubMed, Scopus, Web of Science, and Embase. Case-control studies comparing genotype frequencies of MATN1 and MYO1H in subjects with mandibular prognathism versus Class I controls will be included. The Newcastle-Ottawa Scale (NOS) will be used for quality assessment. If data permits, a meta-analysis will be performed to calculate pooled Odds Ratios (OR) with 95% Confidence Intervals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.009
GPT teacher head0.289
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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