Impact of Adverse Event Solicitation on the Safety Profile of SQ House Dust Mite Sublingual Immunotherapy Tablet
Bibliographic record
Abstract
BACKGROUND: It has been recommended that sublingual immunotherapy (SLIT) safety be assessed using solicited adverse event (AE) collection methods. OBJECTIVES: The objectives of this study were to describe the impact on the safety profile of SQ house dust mite (HDM) SLIT-tablet (12 SQ-HDM dose) when prespecified local application site reactions were solicited versus unsolicited, and discuss ramifications of AE solicitation. METHODS: Subjects were randomized to daily 12 SQ-HDM or placebo for up to 52 weeks in 4 double-blinded, multicenter trials. In one trial (NCT01700192; N = 1272), subjects documented daily the presence or absence of 15 World Allergy Organization-defined local application site reactions using a structured questionnaire of closed-ended questions (solicited AEs). Subjects in the other trials were not asked about specific AEs (unsolicited AEs), and AE data were pooled (N = 1287). Analysis was limited to adults aged 18 to 65 years. RESULTS: Whether AEs were solicited or unsolicited, the most common AEs leading to study discontinuation with 12 SQ-HDM were throat irritation and oral pruritus. Approximately 95% of treatment-related AEs were mild to moderate. Placebo-subtracted frequencies of local application site reactions associated with 12 SQ-HDM were higher when solicited versus unsolicited (ie, throat irritation, 46% vs 13%, respectively; oral pruritus, 47% vs 17%; ear pruritus, 40% vs 4%; mouth swelling, 8% vs 2%; tongue ulceration, 10% vs 0%; mouth ulceration, 7% vs <1%). CONCLUSIONS: Qualitatively, the safety profile of 12 SQ-HDM was similar when AEs were solicited versus unsolicited; hence, solicitation did not alter the safety profile. Higher observed frequencies of local application site reactions with AE solicitation may be partly due to suggestive reporting bias, as observed in placebo-treated subjects.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".