Annual Scientific Meeting, Winnipeg, September 22-25, 2005
Bibliographic record
Abstract
Background: Peanut allergy affects 0.5 to 1% of the general population.Despite enormous efforts to educate peanut allergic patients and their families and increasing public awareness of hidden sources of peanuts, accidental exposures to peanuts still happen.The aim of this study was to calculate the cumulative risk of accidental exposures to peanut among peanut allergic patients and to study their effect on peanut-specific IgE levels (PN-IgE).Methods: This was a retrospective chart review of peanut allergic patients followed up at LHSC from 1997 to 2004.Peanut allergic patients who were less than 15 years old at the time of first reaction to peanut and had serial measurements of PN-IgE were included in the study.Diagnosis of peanut allergy was based on significant history of acute reaction and positive SPT/PN-IgE to peanut.Statistical Analysis: Cumulative risk of accidental exposures and time to significant change of PN-IgE were calculated using Kaplan-Meiers survival curves.Comparisons were made using the log-rank statistics.Results: Medical charts from 118 peanut allergic patients were reviewed (median age at first reaction was 1.5 years old, baseline PN-IgE value was 18.75 kU A /L). Ninety-four patients decreased their PN-IgE levels, whereas 24 experienced elevation of PN-IgE.There were 88 reported accidental exposures in 64 patients and 17 patients experienced more than one accidental exposure.Calculated cumulative risk of reported accidental exposures at 1, 5 and 10 years post initial reaction was 24.8%, 34% and 65.1%, respectively.Conclusion: The predicting factors that determine resolution vs persistence of peanut allergy remain unknown and further studies are required.In conjunction with the avoidance of peanuts, education concerning early recognition of accidental exposures and knowledge of how to manage such reactions remain the main focus of peanut allergic patients' care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.342 | 0.227 |
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".