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Bilateral Cleft Lip and Palate: Improved Maxillary and Dental Development

2006· article· en· W2066401784 on OpenAlexaff
R.A. Latham

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCrossbiteDentistryOrthodonticsMalocclusionOcclusionIncisorSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Maxillary growth deficiency is still a problem in children with repaired cleft lip-cleft palate, and little progress has been made on research for its prevention. An increase in the early diagnosis of maxillary growth deficiency prompted this study to obtain an early indication of the efficacy of a new protocol for better dental and maxillary development. METHODS: A new method for repair of the hard palate in infants with bilateral cleft lip-cleft palate that made innovative use of mucosa of the nasal septum and involved less extensive surgery was coupled with the repair of the lip at 18 months instead of at 8 months. Twelve consecutive cases were compared with 12 cases treated by the previous method. Records of dental occlusion and lateral head radiographs at 5 years were obtained prospectively for the new treatment group and retrospectively for the previous group. RESULTS: In the previous group, there was a high incidence of dental crossbite. Incisor crossbite was present in 10 of the 12 (83 percent), and all 12 had one or both cuspids in crossbite. In the new treatment group, the incisor crossbite was reduced to four of 12 cases (33 percent), with cuspid crossbite at 50 percent. The new treatment group also showed greater values for cephalometric measures in maxillary length, maxillary prominence, and the ANB angle. CONCLUSION: At the age of 5 years, a definite improvement in dental and maxillary development was evident in the new treatment group.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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 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

Citations15
Published2006
Admission routes1
Has abstractyes

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