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Record W2183061250 · doi:10.1597/06-207.1

A Study of Speech, Language, Hearing, and Dentition in Children with Cleft Lip Only

2008· article· en· W2183061250 on OpenAlexaff
Linda D. Vallino, Ronald M. Zuker, Joseph A. Napoli

Bibliographic record

VenueThe Cleft Palate-Craniofacial Journal · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineAudiologyDentitionDentistryHearing lossSupernumeraryRetrospective cohort studyOrthodonticsSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the prevalence of speech, language, hearing, and dental problems in children with an initial diagnosis of isolated cleft lip only (CL), for which evidence-based practice can be developed. DESIGN: Retrospective chart review of 95 patients with cleft lip (age range, 2.8 to 3.7 years; mean, 3.1 years). RESULTS: Speech and language impairment was documented in 13% and 18% of the patients, respectively. Thirty-three percent of the children presented with middle ear effusion. Thirteen percent had abnormal hearing. With one exception, the type and degree of hearing loss was a mild conductive loss most often attributed to the presence of effusion. Dental and/or occlusal anomalies were documented in 62% of the patients. A supernumerary tooth was the most frequently occurring dental anomaly and crossbite the most frequently occurring occlusal anomaly. Two children had a submucous cleft palate. Resonance was abnormal in 5% of the children. CONCLUSION: Children with an initial diagnosis of CL need to be monitored by the interdisciplinary team for speech, language, ear disease, hearing, and dentition beginning in infancy and followed until all management needs are met.

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)0.001
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.279
Teacher spread0.262 · 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

Citations57
Published2008
Admission routes1
Has abstractyes

Explore more

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