Skin prick/puncture testing in North America: a call for standards and consistency
Bibliographic record
Abstract
BACKGROUND: Skin prick/puncture testing (SPT) is widely accepted as a safe, dependable, convenient, and cost-effective procedure to detect allergen-specific IgE sensitivity. It is, however, prone to influence by a variety of factors that may significantly alter test outcomes, affect the accuracy of diagnosis, and the effectiveness of subsequent immunotherapy regimens. Proficiency in SPT administration is a key variable that can be routinely measured and documented to improve the predictive value of allergy skin testing. METHODS: Literature surveys were conducted to determine the adherence to repeated calls for development and implementation of proficiency testing standards in the 1990's, the mid-2000's and the 2008 allergy diagnostics practice parameters. RESULTS: Authors publishing clinical research in peer-reviewed journals and conducting workshops at annual scientific meetings have recommended proficiency testing based primarily on its potential to reduce variability, minimize confounding test results, and promote more effective immunotherapeutic treatments. Very few publications of clinical studies, however, appear to report proficiency testing data for SPT performance. Allergen immunotherapy recommendations are updated periodically by the Joint Task Force on Practice Parameters representing the American Academy of Allergy, Asthma and Immunology (AAAAI), the American College of Allergy, Asthma and Immunology (ACAAI), and the Joint Council of Allergy, Asthma and Immunology (JCAAI). CONCLUSIONS: Despite consensus that all staff who perform SPT should meet basic quality assurance standards that demonstrate their SPT proficiency, the gap between recommendations and daily practice persists. By embracing standards, the accuracy of SPT and allergy diagnosis can be optimized, ultimately benefiting patients with allergic disease.
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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 | 0.274 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 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".