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Record W4396224958 · doi:10.1177/19458924241251387

Predictive Value of Nasal Nitric Oxide for Diagnosing Eosinophilic Chronic Rhinosinusitis: A Systematic Review and Meta-Analysis

2024· review· en· W4396224958 on OpenAlexaboutno aff
Do Hyun Kim, Hyesoo Shin, Gulnaz Stybayeva, Se Hwan Hwang

Bibliographic record

VenueAmerican Journal of Rhinology and Allergy · 2024
Typereview
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsnot available
FundersNational Research Foundation of Korea
KeywordsMedicineConfidence intervalChronic rhinosinusitisInternal medicineMeta-analysisOdds ratioGastroenterologyDiagnostic odds ratioEosinophilicNasal polypsReceiver operating characteristicPathology

Abstract

fetched live from OpenAlex

Objectives The primary aim of this study was to assess disparities in nasal nitric oxide (NO) levels between individuals diagnosed with eosinophilic chronic rhinosinusitis (ECRS) and those without ECRS. The second aim was to ascertain the comparative predictive efficacy of these nasal NO levels for the presence of ECRS. Methods A systematic analysis was conducted on relevant studies that compared nasal NO levels in individuals with ECRS and those without. Furthermore, the discriminatory capacity of nasal NO in distinguishing ECRS from non-ECRS cohorts was quantified. The risk of bias across studies was evaluated utilizing the Newcastle-Ottawa scale. Results The comprehensive review encompassed a total of 5 studies involving 470 participants. Findings revealed that patients diagnosed with ECRS exhibited significantly higher levels of nasal NO, as measured in parts per billion (ppb), compared to their non-ECRS patients. The mean difference was 130.03 ppb (95% confidence interval: [66.30, 193.75], I2 = 58.7%). The diagnostic odds ratio for nasal NO in identifying ECRS was 9.29 ([5.85, 14.75], I2 = 26.4%). The area under the summary receiver operating characteristic curve was 0.82. The correlation between sensitivity and false positive rate was 0.53, suggesting a lack of heterogeneity. Sensitivity, specificity, negative predictive value, and positive predictive value were 69% ([0.55, 0.79], I2 = 77.0%), 83% ([0.73, 0.90], I 2 = 68.5%), 77% ([0.69, 0.83], I 2 = 50.1%), and 75% ([0.67, 0.82], I 2 = 41.5%), respectively. Conclusion Nasal NO has the potential as a noninvasive diagnostic measure and endotype tool for ECRS.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0110.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.351
Teacher spread0.311 · 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 designMeta-analysis
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

Citations8
Published2024
Admission routes1
Has abstractyes

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