Diagnostic accuracy of skin-prick testing for allergic rhinitis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Allergic rhinitis is the most common form of allergy worldwide. The accuracy of skin testing for allergic rhinitis is still debated. Our primary objective was to evaluate the diagnostic accuracy of skin-prick testing for allergic rhinitis using the nasal provocation as the reference standard. We also evaluated the diagnostic accuracy of intradermal testing as a secondary objective. METHODS: We searched EBM Reviews from 2005 to March 2015; Embase from 1980 to March 2015; and Ovid MEDLINE(R) from 1946 to until March 2015. We included any study with at least 10 subjects including children. We excluded non-English studies. We performed data extraction and quality assessment using the QUADAS-2 tool. RESULTS: We meta-analysed seven studies assessing the accuracy of skin-prick testing using the bivariate random-effects model, including a total of 430 patients. The pooled estimate for sensitivity and specificity for skin-prick testing was 85 and 77 % respectively. We did not pool results for intradermal testing due to few number of studies (n = 4), each with very small sample size. Of these, two evaluated the accuracy of intradermal testing in confirming skin-prick testing results, with sensitivity ranging from 27 to 50 % and specificity ranging from 60 to 100 %. The other two evaluated the accuracy of intradermal testing as a stand-alone test for diagnosing allergic rhinitis with sensitivity ranging from 60 to 79 % and specificity ranging from 68 to 69 %. CONCLUSIONS: Findings from this review suggest that skin-prick testing is accurate in discriminating subjects with or without allergic rhinitis.
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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 | 0.026 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".