Diagnostic validity of specific immunoglobulin E levels to alpha-gal in alpha-gal syndrome: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: The diagnosis of Alpha-gal Syndrome (AGS) is based on the presence of symptoms after being exposed to potential sources of alpha-gal together with values of specific IgE (sIgE) to alpha-gal ≥ 0.1 kUA/L or ≥ 0.35 kUA/L. The aim of this study was to evaluate the diagnostic validity of sIgE levels to alpha-gal ≥ 0.1 kUA/L for identifying AGS. METHODS: This was a cross-sectional analysis of adult patients with available data on sIgE levels to alpha-gal, classified into two groups according to the presence (Group 1) or absence (Group 2) of symptoms after being exposed to potential sources of alpha-gal. Values of sIgE to alpha-gal ≥ 0.1 kUA/l were considered a positive result. A descriptive analysis of internal and external validity parameters was performed in the entire population and adjusted by sex. RESULTS: The study included 33 individuals in Group 1 and 65 in Group 2, with a mean age of around 47 years. The analysis of internal validity parameters revealed a high sensitivity, specificity, and positive probability ratio, with higher sensitivity in men and higher specificity in women. The analysis of external validity parameters showed a high negative predictive value and global value in all populations and both sexes. However, the positive predictive value was relatively high in men, but low in women. CONCLUSIONS: Our results suggest that sIgE levels ≥ 0.1 kUA/L may be a useful tool for the diagnosis of AGS, although other factors and diagnostic techniques should also be considered.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".