Dental anomalies in craniofacial microsomia: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To provide an overview on the prevalence and types of dental anomalies in patients with craniofacial microsomia (CFM). Eligibility criteria: Inclusion criteria were CFM and dental anomalies. The following data were extracted: number of patients, methodology, mean age, sex, affected side, severity of mandibular hypoplasia, dentition stage and dental anomalies. INFORMATION SOURCES: Cochrane, EMBASE, PubMed, MEDLINE Ovid, Web of Science, CINAHL EBSCOhost and Google Scholar, searched until the 30 August 2019. Risk of bias: The quality was examined with the OCEBM Levels of Evidence. INCLUDED STUDIES: In total, 13 papers were included: four retrospective cohort studies, four prospective cohort studies, four case-control studies and one case series. Synthesis of results: The studies reported information on dental agenesis, delayed dental development, tooth size anomalies, tooth morphology and other dental anomalies. Description of the effect: Dental anomalies are more often diagnosed in patients with CFM than in healthy controls and occur more often on the affected than on the non-affected side. Strengths and limitations of evidence: This is the first systematic review study on dental anomalies in CFM. However, most articles were of low quality. INTERPRETATION: Dental anomalies are common in CFM, which might be linked to the development of CFM. The pathophysiology of CFM is not entirely clear, and further research is needed.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".