Craniofacial syndromes and class III phenotype: common genotype fingerprints? A scoping review and meta-analysis
Bibliographic record
Abstract
Skeletal Class III (SCIII) is among the most challenging craniofacial dysmorphologies to treat. There is, however, a knowledge gap regarding which syndromes share this clinical phenotype. The aims of this study were to: (i) identify the syndromes affected by the SCIII phenotype; (ii) clarify the involvement of maxillary and/or mandibular structures; (iii) explore shared genetic/molecular mechanisms. A two-step strategy was designed: [Step#1] OMIM, MHDD, HPO, GeneReviews and MedGen databases were explored; [Step#2]: Syndromic conditions indexed in [Step#1] were explored in Medline, Pubmed, Scopus, Cochrane Library, WOS and OpenGrey. Eligibility criteria were defined. Individual studies were assessed for risk of bias using the New Ottawa Scale. For quantitative analysis, a meta-analysis was conducted. This scoping review is a hypothesis-generating research. Twenty-two studies met the eligibility criteria. Eight syndromes affected by the SCIII were targeted: Apert syndrome, Crouzon syndrome, achondroplasia, X-linked hypohidrotic ectodermal dysplasia (XLED), tricho-dento-osseous syndrome, cleidocranial dysplasia, Klinefelter and Down syndromes. Despite heterogeneity between studies [p < 0.05], overall effects showed that midface components were affected in Apert and Down Syndromes, lower face in Klinefelter Syndrome and midface and lower face components in XLED. Our review provides new evidence on the craniofacial characteristics of genetically confirmed syndromes exhibiting the SCIII phenotype. Four major regulatory pathways might have a modulatory effect on this phenotype. IMPACT: What does this review add to the existing literature? To date, there is no literature exploring which particular syndromes exhibit mandibular prognathism as a common trait. Through this research, it was possibly to identify the particular syndromes that share the skeletal Class III phenotype (mandibular prognathism) as a common trait highlighting the common genetic and molecular pathways between different syndromes acknowledging their impact in craniofacial development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".