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Record W2349326089 · doi:10.1097/scs.0000000000002506

Chronic Rhinosinusitis With Massive Polyposis Causing Proptosis Requiring Craniofacial Resection

2016· article· en· W2349326089 on OpenAlexaff
Mazda K. Turel, Christopher J. Chin, Allan Vescan, Fred Gentili

Bibliographic record

VenueJournal of Craniofacial Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsToronto Western HospitalMount Sinai Hospital
Fundersnot available
KeywordsMedicineNasal polypsChronic rhinosinusitisCraniofacialSinusitisSkullBone erosionSurgeryResectionDermatologyInternal medicine

Abstract

fetched live from OpenAlex

Chronic rhinosinusitis (CRS) is a common health problem in the Western world. CRS is classified as CRS with (CRSwNP) and without (CRSsNP) nasal polyps. A less common third type is allergic fungal sinusitis, which often presents with polyps and, not infrequently, skull base erosion. Most patients are successfully managed with maximal medical therapy or endoscopic approaches. There are currently no reports of CRSwNPs resulting in fibro-osseous thickening and proptosis in the English literature. As such, the authors report a case of a 33-year-old man who underwent a craniofacial resection with drilling of the hyperostosed bone, which led to resolution of the proptosis and nasal symptoms. In an era where endoscopic surgery is the standard surgical approach for CRSwNP, this case highlights the need for open skullbase approaches for this condition due to the extensive and recalcitrant nature of the disease. While the majority of patients can be dealt with endoscopically, the authors highlight the importance of having the open approach in the otolaryngologists' armamentarium for patients of recalcitrant and extensive CRSwNP.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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 designCase report
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

Citations2
Published2016
Admission routes1
Has abstractyes

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