Skin Testing for Allergic Rhinitis: A Health Technology Assessment.
Bibliographic record
Abstract
BACKGROUND: Allergic rhinitis is the most common type of allergy worldwide. The accuracy of skin testing for allergic rhinitis is still debated. This health technology assessment had two objectives: to determine the diagnostic accuracy of skin-prick and intradermal testing in patients with suspected allergic rhinitis and to estimate the costs to the Ontario health system of skin testing for allergic rhinitis. METHODS: We searched All Ovid MEDLINE, Embase, and Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, CRD Health Technology Assessment Database, Cochrane Central Register of Controlled Trials, and NHS Economic Evaluation Database for studies that evaluated the diagnostic accuracy of skin-prick and intradermal testing for allergic rhinitis using nasal provocation as the reference standard. For the clinical evidence review, data extraction and quality assessment were performed using the QUADAS-2 tool. We used the bivariate random-effects model for meta-analysis. For the economic evidence review, we assessed studies using a modified checklist developed by the (United Kingdom) National Institute for Health and Care Excellence. We estimated the annual cost of skin testing for allergic rhinitis in Ontario for 2015 to 2017 using provincial data on testing volumes and costs. RESULTS: We meta-analyzed seven studies with a total of 430 patients that assessed the accuracy of skin-prick testing. The pooled pair of sensitivity and specificity for skin-prick testing was 85% and 77%, respectively. We did not perform a meta-analysis for the diagnostic accuracy of intradermal testing due to the small number of studies (n = 4). Of these, two evaluated the accuracy of intradermal testing in confirming negative skin-prick testing results, with sensitivity ranging from 27% to 50% and specificity ranging from 60% to 100%. The other two studies evaluated the accuracy of intradermal testing as a stand-alone tool for diagnosing allergic rhinitis, with sensitivity ranging from 60% to 79% and specificity ranging from 68% to 69%. We estimated the budget impact of continuing to publicly fund skin testing for allergic rhinitis in Ontario to be between $2.5 million and $3.0 million per year. CONCLUSIONS: Skin-prick testing is moderately accurate in identifying subjects with or without allergic rhinitis. The diagnostic accuracy of intradermal testing could not be well established from this review. Our best estimate is that publicly funding skin testing for allergic rhinitis costs the Ontario government approximately $2.5 million to $3.0 million per year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".