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Record W2999564152 · doi:10.1002/alr.22520

Aspirin desensitization therapy in aspirin‐exacerbated respiratory disease: a systematic review

2020· review· en· W2999564152 on OpenAlexaff
Natasha Larivée, Christopher J. Chin

Bibliographic record

VenueInternational Forum of Allergy & Rhinology · 2020
Typereview
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsDalhousie UniversitySaint John Regional Hospital
Fundersnot available
KeywordsMedicineAspirinNasal polypsDesensitization (medicine)MEDLINECINAHLRandomized controlled trialObservational studySystematic reviewInternal medicineCochrane LibraryMeta-analysisDiseaseChronic rhinosinusitisIntensive care medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) represents an aggressive form of chronic rhinosinusitis with nasal polyposis that is notoriously challenging to treat. There is evidence to suggest desensitization to aspirin may improve symptomatology and disease control in these patients. The goal of our study was to critically appraise the literature on this topic and assess the effect of desensitization on sinonasal symptomatology. METHODS: We searched EMBASE, CINAHL, MEDLINE, and the Cochrane Library for relevant literature. Studies were included if they were observational studies or randomized, controlled trials, had n > 1, and were published in English or French. Studies were excluded if they were systematic reviews. We assessed study for quality and presence of common sources of bias. RESULTS: Twenty-four studies met the inclusion criteria. In general, polyp size, polyp recurrence, nasal symptom scores, sense of smell, number of acute rhinosinusitis episodes, and systemic steroid use improved when patients were desensitized. The vast majority of studies recommend desensitization. CONCLUSION: There is mounting evidence that aspirin desensitization is a valuable adjunct to treat sinonasal symptoms in the treatment of patients who have AERD.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.675
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.361
Teacher spread0.304 · 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 designSystematic review
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

Citations22
Published2020
Admission routes1
Has abstractyes

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