Nebulised furosemide for the treatment of patients with obstructive lung disease: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Obstructive lung diseases (OLDs) such as asthma and chronic obstructive pulmonary disease are major global sources of morbidity and mortality. Current treatments broadly include bronchodilators such as beta agonists/antimuscarinics and anti-inflammatory agents such as steroids. Despite therapy patients still experience exacerbations of their diseases and overall decline over time. Nebulised furosemide may have a novel use in the treatment of OLD. Multiple small studies have shown improvement in pulmonary function as well as dyspnoea. This systematic review will aim to summarise and analyse the existing literature on nebulised furosemide use in OLD to guide treatment and future studies. METHODS AND ANALYSIS: We will identify all experimental studies using nebulised/inhaled furosemide in patients with asthma or chronic obstructive pulmonary disease that report any outcome. Databases will include EMBASE, MEDLINE, Cochrane Database of Systematic Reviews, ACP Journal Club, Database of Abstracts of Reviews of Effects, Cochrane Clinical Answers, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register, Health Technology Assessment and the NHS Economic Evaluation Database (1995-2015). We will also search ClinicalTrials.gov and the WHO-International Clinical Trials Registry Platform. Two reviewers will independently determine trial eligibility. For each included trial, we will perform duplicate independent data extraction, risk of bias assessment and evaluation of the quality of evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. ETHICS AND DISSEMINATION: Ethical approval will not be applicable to this systematic review. The results of the study will be communicated through publication in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42021284680.
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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 | 0.062 | 0.057 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.012 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.089 | 0.011 |
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".