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Record W4403054791 · doi:10.58931/cait.2022.2126

A New Era: Exploring the Role of Monoclonal Antibody Therapy in the Treatment of Chronic Rhinosinusitis with Nasal Polyposis

2022· article· en· W4403054791 on OpenAlexfundno aff
Ameen Biadsee, Leigh J. Sowerby

Bibliographic record

VenueCanadian allergy & immunology today. · 2022
Typearticle
Languageen
FieldMedicine
TopicOtitis Media and Relapsing Polychondritis
Canadian institutionsnot available
FundersMcMaster University
KeywordsChronic rhinosinusitisMonoclonal antibodyMedicineImmunologyMonoclonal antibody therapyNasal polypsAntibodyDermatology

Abstract

fetched live from OpenAlex

Chronic rhinosinusitis (CRS), in its simplest form, is inflammation of the paranasal sinuses that has been present for more than three months. The clinical diagnosis is characterized by nasal obstruction/congestion/discharge, facial pain and decreased/absent smell with signs of inflammation in the sinonasal mucosa on endoscopy or computed tomography. An impaired sense of smell and olfactory loss is a cardinal feature of patients with nasal polyps. CRS affects about 5-12% of the population according to recent epidemiological studies, with a peak prevalence of 16% between the ages of 50-59. While the symptoms are often downplayed by patients themselves, the impact on quality of life has been shown to be on par with congestive heart failure, moderate chronic obstructive pulmonary disorder and Parkinson’s disease. The most common extra-nasal sequelae are fatigue and depression, with approximately half of patients surveyed reporting fatigue and one-quarter reporting depression. The societal impact is significant with annual rates of absenteeism estimated at 24.6 days a year, and at an overall productivity cost estimated at $10,077 per patient.

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 score0.699
Threshold uncertainty score0.860

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)0.0010.000

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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