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Record W4412819312 · doi:10.1177/19458924251364570

Transcaruncular Approach With Orbital Protection for Resection of Sinonasal Lesions: How I do it

2025· article· en· W4412819312 on OpenAlexaff
Jakob L. Fischer, Kelsey A. Roelofs, Persiana S. Saffari, Jeffrey D. Suh, Daniel B. Rootman, Robert A. Goldberg, Jivianne T. Lee

Bibliographic record

VenueAmerican Journal of Rhinology and Allergy · 2025
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDissection (medical)Orbit (dynamics)SurgeryAnatomy

Abstract

fetched live from OpenAlex

BackgroundMinimally invasive techniques for the resection of sinonasal masses have become increasingly important over the past few decades. Sinonasal disease involving the lamina papyracea remains difficult to manage given the risk of injury to critical orbital structures and hemorrhage from nearby vessels.ObjectiveDetail the transcaruncular approach with orbital protection for the resection of benign and malignant sinonasal pathologies.MethodsDescription of surgical technique and presentation of 2 representative cases that were successfully managed with this surgical technique.ResultsThe transcaruncular approach involves incising the lateral 1/3 of the caruncle in a vertical plane between the upper and lower puncta. Dissection is then carried through the retrocaruncular fascia posterior to Horner's muscle to the posterior lacrimal crest along the medial orbital wall. Dissection can then be performed in a subperiosteal or supraperiosteal plane with subsequent ligation of the anterior ethmoidal artery. Once dissected, a nylon sheet used for orbital reconstruction and colored orbital shield can then be placed to aid in protection and visualization or orbital contents during endonasal tumor resection.ConclusionThe transcaruncular approach with orbital protection provides intraoperative protection of the orbital contents, allowing for safer removal of the mass irrespective of integrity of the lamina papyracea.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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 designOther design
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

Citations1
Published2025
Admission routes1
Has abstractyes

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