MétaCan
Menu
Back to cohort
Record W1978586409 · doi:10.1097/scs.0b013e3181b6c74a

Design Features and Simple Methods of Incorporating Nasal Stents in Presurgical Nasoalveolar Molding Appliances

2009· article· en· W1978586409 on OpenAlexaff
Sunjay Suri

Bibliographic record

VenueJournal of Craniofacial Surgery · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineNasal cartilagesClinical PracticeStentDentistryOrthodonticsMolding (decorative)ColumellaMedical physicsNoseSurgeryRhinoplastyPhysical therapyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Presurgical nasoalveolar molding (NAM) in the orofacial orthopedic treatment of unilateral clefts of the lip and palate aims to align and approximate the maxillary hemialveolar segments and simultaneously support and mold the deformed nasal cartilages, correct and center nasal tip projection, and lengthen the deficient cleft-side columella in early infancy, before the primary reparative lip surgery. A number of techniques of achieving these objectives have been described in the literature and are increasingly being practiced by cleft care teams around the world. However, a detailed description of the nasal stent is lacking in the literature and needs to be elucidated to facilitate greater usage of presurgical NAM in contemporary practice. This report fills this void by providing an analytical description of the different parts of the nasal stent; clarifies their desirable design features, anatomic correlations, and clinical importance; and illustrates in a step-by-step manner simple direct and indirect methods of incorporating a nasal stent, improvised by the author in his practice, that can be used with any of the contemporary NAM appliances and techniques. From the simple methods described, clinicians will be enabled to select one that may be most easily adaptable to their preferred appliance and clinical setting.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.040
GPT teacher head0.357
Teacher spread0.317 · 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 designBench or experimental
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

Citations20
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Craniofacial SurgerySame topicCleft Lip and Palate ResearchFrench-language works237,207