Erratum to "IgE allergy diagnostics and other relevant tests in allergy, a World Allergy Organization position paper" [World Allergy Organ J 13/2 (2020) 100080]
Bibliographic record
Abstract
The publisher regrets we have been made aware of the below errors:1)In Table 15, row NOVEOS chemiluminescent assay is written “utilizes 40 μL (0.04 ml) of sample per result”. The correct value would be “4 μL (0.004 ml)" of sample per result.2)In Table 16, is written “NOVEOS menu has 79 available allergens, consisting of 69 extracts and 10 molecular allergens”. It should say “NOVEOS menu continues to increase and it has 152 total allergens with 108 extracts and 44 components".3)In Table 15, row “Euroimmun”, column “Patient's serum”, is written “1000 ml”. The correct value would be “0.1 ml (-0.4 ml)”. The publisher would like to apologise for any inconvenience caused. IgE allergy diagnostics and other relevant tests in allergy, a World Allergy Organization position paperWorld Allergy Organization JournalVol. 13Issue 2PreviewCurrently, testing for immunoglobulin E (IgE) sensitization is the cornerstone of diagnostic evaluation in suspected allergic conditions. This review provides a thorough and updated critical appraisal of the most frequently used diagnostic tests, both in vivo and in vitro. It discusses skin tests, challenges, and serological and cellular in vitro tests, and provides an overview of indications, advantages and disadvantages of each in conditions such as respiratory, food, venom, drug, and occupational allergy. Full-Text PDF Open Access
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.103 | 0.124 |
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".