Anti-IgE monoclonal antibody therapy for the treatment of chronic rhinosinusitis: a systematic review
Bibliographic record
Abstract
BACKGROUND: Several options are available for the treatment of chronic rhinosinusitis (CRS), but disease control remains elusive for many patients. Recently, literature has emerged describing anti-IgE monoclonal antibody as a potential therapy for CRS. However, its effectiveness and safety are not well known. The purpose of this systematic review was to assess the effectiveness and safety of anti-IgE therapy and to identify evidence gaps that will guide future research for the management of CRS. METHODS: Methodology was registered with PROSPERO (No. CRD42014007600). A comprehensive search was performed of standard bibliographic databases, Google Scholar, and clinical trials registries. Only randomized controlled trials assessing anti-IgE therapy in adult patients for the treatment of CRS were included. Two independent reviewers extracted data using a pre-defined extraction form and performed quality assessment using the Cochrane risk of bias tool and the GRADE framework. RESULTS: Two studies met our inclusion criteria. When comparing anti-IgE therapy to placebo, there was a significant difference in Lund-McKay score (p = 0.04) while no difference was seen for percent opacification on computed tomography (CT). At 16 weeks, treatment led to a decrease in clinical polyp score. No significant difference was seen with regard to quality of life (Total Nasal Symptom Severity (TNSS), p < 0.21; Sinonasal Outcome Test 20 (SNOT-20), p < 0.60), and no serious complications were reported in either trial. Based on the quality assessment, studies were deemed to be of moderate risk of bias and a low overall quality of evidence. CONCLUSIONS: There is currently insufficient evidence to determine the effectiveness of anti-IgE monoclonal antibody therapy for the treatment of CRS.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".