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Record W4311175873 · doi:10.4193/rhin22.344

The EUFOREA pocket guide for chronic rhinosinusitis

2022· article· en· W4311175873 on OpenAlexaff
Peter W. Hellings, W.J. Fokkens, R. Orlandi, G.F. Adriaensen, I. Alobid, F.M. Baroody, L. Bjermer, B.A. Senior, A. Cervin, N.A. Cohen, J. Constantinidis, Eugenio De Corso, M. Desrosiers, Z. Diamant, R.G. Douglas, S. Gane, P. Gevaert, J.K. Han, Richard J. Harvey, C. Hopkins, R.C. Kern, Basile N. Landis, J.T. Lee, S.E. Lee, A. Leunig, V.J. Lund, B. Manuel-Sprekelsen, J. Mullol, Carl Philpott, E. Prokopakis, Sietze Reitsma, D. Ryan, G. Scadding, R.J. Schlosser, A. Steinsvik, P.V. Tomazic, E. Van Staeyen, T. Van Zele, O. Vanderveken, A-S. Viskens, Diego M. Conti, M. Wagenmann

Bibliographic record

VenueRhinology Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsUniversité de Montréal
FundersSanofi GenzymeRegeneron PharmaceuticalsSanofi
KeywordsMedicineRhinologyChronic rhinosinusitisNasal polypsSinusitisPosition paperAsthmaIntensive care medicinePopulationQuality of life (healthcare)OtorhinolaryngologyEnvironmental healthInternal medicinePathologySurgery

Abstract

fetched live from OpenAlex

Chronic rhinosinusitis (CRS) is known to affect around 5 % of the total population, with major impact on the quality of life of those severely affected (1). Despite a substantial burden on individuals, society and health economies, CRS often remains underdiagnosed, under-estimated and under-treated (2). International guidelines like the European Position Paper on Rhinosinusitis and Nasal Polyps (EPOS) (3) and the International Consensus statement on Allergy and Rhinology: Rhinosinusitis 2021 (ICAR) (4) offer physicians insight into the recommended treatment options for CRS, with an overview of effective strategies and guidance of diagnosis and care throughout the disease journey of CRS.

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)0.003
metaresearch head score (Gemma)0.012
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.160
Threshold uncertainty score0.535

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.1600.139

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 head0.017
GPT teacher head0.300
Teacher spread0.283 · 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
GenreOther

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

Citations61
Published2022
Admission routes1
Has abstractyes

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