MétaCan
Menu
Back to cohort
Record W1854634845 · doi:10.1002/alr.21344

Update on evidence‐based reviews with recommendations in adult chronic rhinosinusitis

2014· review· en· W1854634845 on OpenAlexaff
Richard R. Orlandi, Timothy L. Smith, Bradley F. Marple, Richard J. Harvey, Peter H. Hwang, Robert C. Kern, Todd T. Kingdom, Amber Luong, Luke Rudmik, Brent A. Senior, Elina Toskala, David W. Kennedy

Bibliographic record

VenueInternational Forum of Allergy & Rhinology · 2014
Typereview
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRhinologyMedicineChronic rhinosinusitisEvidence-based medicineFoundation (evidence)Alternative medicineMEDLINEBest practiceBest evidencePatient careIntensive care medicineFamily medicineNursingPathologyOtorhinolaryngologySurgeryManagement

Abstract

fetched live from OpenAlex

Chronic rhinosinusitis (CRS) has a significant impact not only on individuals who are afflicted but also on society as a whole. An increasing emphasis is being placed on incorporating the best available evidence into the care of patients, in association with an individual clinician's expertise and the patient's values. Recent evidence-based reviews with recommendations (EBRRs) have distilled our knowledge of CRS treatment options and have also pointed out continued gaps in this knowledge. This review synthesizes the findings of 8 EBRRs regarding CRS published in the International Forum of Allergy and Rhinology between 2011 and 2014. The recommendations in this review are based on the best available evidence and are meant to be incorporated into each patient's individual care, along with the practitioner's expertise and the individual patient's values and expectations. It is hoped that the EBRRs, and the process that spawned them, can provide the foundation for future guidelines in the diagnosis and management 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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.381
Teacher spread0.313 · 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.

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

Citations36
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Forum of Allergy & RhinologySame topicSinusitis and nasal conditionsFrench-language works237,207