Allergen immunotherapy for respiratory allergy: Quality appraisal of observational comparative effectiveness studies using the REal Life Evidence AssessmeNt Tool. An EAACI methodology committee analysis
Bibliographic record
Abstract
BACKGROUND: Observational comparative effectiveness studies in allergen immunotherapy (AIT) represent an important evidence source answering research questions that can be challenging to obtain from randomized controlled trials (RCTs), such as long-term benefits of AIT, the effects on asthma prevention and the onset of new allergen sensitizations. However, observational studies are prone to several sources of bias, which limit their reliability.The REal Life Evidence AssessmeNt Tool (RELEVANT) was recently developed to assist in quality appraisal of observational comparative research to enable identification of useful nonrandomized studies to be considered within guideline development. OBJECTIVE: To systematically appraise the quality of published observational comparative AIT studies using RELEVANT. METHODS: Observational studies comparing AIT to pharmacotherapy for respiratory allergies, assessing as outcome measures reduction of symptoms and/or medication use reduction, were retrieved by computerized bibliographic searches. According to RELEVANT, a failure to meet any one of primary items (background, design, measures, analysis, results, discussion/interpretation, and conflict of interest) represents a critical flaw, significantly undermining the validity of the study results. RESULTS: The 14 studies identified supported the benefit of AIT in real-life, which persists after treatment discontinuation. However, none of them met all the 7 primary RELEVANT criteria. The main defects were reported in the design (28.6% of studies), measures and analysis (64.3% of studies), and results (78.6% of studies) items, due to selection bias and lack of methods for adjusting controls. Half of the studies did not report on conflict of interest. CONCLUSION: There is a need for more robust observational research in AIT. RELEVANT appears as an easy-to-use and sensitive tool for quality appraisal in AIT studies.
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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 | 0.522 | 0.671 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.024 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".