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Record W2606725950 · doi:10.1016/j.carj.2016.12.009

Fungal Rhinosinusitis: A Radiological Review with Intraoperative Correlation

2017· review· en· W2606725950 on OpenAlexaff
Elaine Ni Mhurchu, Javier Ospina, Arif Janjua, Jason R. Shewchuk, Alexandra T. Vertinsky

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2017
Typereview
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsSt. Paul's HospitalVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineParanasal sinusesPathologySinus (botany)DiseaseSinusitisChronic rhinosinusitisFungal sinusitisSurgeryBiology

Abstract

fetched live from OpenAlex

The interaction between fungi and the sinonasal tract results in a range of clinical presentations with a broad spectrum of clinical severity. The most commonly accepted classification system divides fungal rhinosinusitis into invasive and noninvasive subtypes based on histopathological evidence of tissue invasion by fungi. Invasive fungal rhinosinusitis is subdivided into acute invasive and chronic invasive categories. The chronic invasive category includes a subcategory of chronic granulomatous disease. Noninvasive fungal disease includes localized fungal colonization, fungal ball, and allergic fungal rhinosinusitis. Noninvasive disease is simply fungal material (or the products of the inflammatory reaction of the sinus mucosa) that fills the sinuses but does not invade tissue. Bone loss is related to expansion of the sinus(es). Invasive disease causes tissue destruction, such that it expands past the bony confines of the sinuses. It can rapidly spread, causing acute necrosis. Alternatively, there may be slow tissue invasion characterized by symptoms confused with normal sinusitis, but destruction of normal nasal and paranasal structures.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.007
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.071
GPT teacher head0.362
Teacher spread0.291 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations65
Published2017
Admission routes1
Has abstractyes

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