Investigating fractional exhaled nitric oxide (FeNO) in chronic obstructive pulmonary disease (COPD) and asthma–COPD overlap (ACO): a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: During the last decade, many articles have been published, including reviews on fractional exhaled nitric oxide (FeNO) use and utility in clinical practice and for monitoring and identifying eosinophilic airway inflammation, especially in asthma, and evaluating corticosteroid responsiveness. However, the exact role of FeNO in patients with chronic obstructive pulmonary disease (COPD) and its ability to distinguish patients with COPD and those having concomitant asthma, that is, asthma-COPD overlap (ACO) is still unclear and needs to be defined. Due to the broad topics of FeNO in chronic airway disease, we undertook a scoping review. The present article describes the protocol of a scoping review of peer-reviewed published literature specific to FeNO in COPD/ACO over the last decade. METHODS AND ANALYSIS: 's framework as guides. We searched a variety of databases, including Medline, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Web of Science, and BioSciences Information Service (BIOSIS) on 29 June 2016. Additional studies will be recognised by exploring the reference list of identified eligible studies. Screening of eligible studies will be independently performed by two reviewers and any disagreement will be solved by the third reviewer. We will analyse the gathered data from article bibliographies and abstracts. ETHICS AND DISSEMINATION: To investigate the body of published studies regarding the role of FeNO in patients with COPD and its usefulness in the clinical setting, a scoping review can be used as a modern and pioneer model, which does not need ethics approval. By this review, new insights for conducting new research specific to FeNO in COPD/ACO population will emerge. The results of this study will be reported in the scientific meetings and conferences, which aim to provide information to the clinicians, primary care providers and basic science researchers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.114 | 0.088 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".