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Endoscopic Anatomy of the Orbital Floor and Maxillary Sinus

2008· article· en· W114432094 on OpenAlexaff
Corey C. Moore, Matthew Bromwich, Kathy Roth, Damir B. Matic

Bibliographic record

VenueJournal of Craniofacial Surgery · 2008
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineMaxillary sinusAnatomyCadaverOstiumOrbital FractureEthmoidectomySoft tissueMedial wallRadiologySurgery

Abstract

fetched live from OpenAlex

Endoscopic repair of orbital blow-out fractures could become a predictable and efficient treatment alternative to traditional methods. However, maxillary sinus endoscopy provides a complex and disorienting view of the orbital floor. To be a useful and consistent technique for providing access to the orbital floor, specific knowledge of maxillary endoscopic anatomy is required. The purpose of the study was to provide an anatomic description of the orbital floor via the endoscopic approach. Objectives include defining consistent landmarks for use in endoscopic repair of orbital floor fractures. Using 0- and 30-degree rigid endoscopes, 6 fresh cadavers (12 maxillary sinuses) were examined via a standard Caldwell-Luc approach. Computed tomographic scans, plastic molds, and digital images were used to compare observable averages within bony anatomy. Potential bony landmarks were correlated with soft-tissue anatomy in fresh specimens. The maxillary ostium, orbital floor, and lateral ethmoid air cells were visualized, and their structures were described. Observations were made in relation to the anatomy of the orbital floor and maxillary sinus, including fracture pattern and force transmission pathways. An "orbitomaxillary" sinus bony thickening was identified and described for the first time. This study provides the basis for further refinement of surgical technique and opens the door for future clinical trials using endoscopic repair.

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 score0.010
Threshold uncertainty score0.223

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 head0.020
GPT teacher head0.262
Teacher spread0.243 · 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
Published2008
Admission routes1
Has abstractyes

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