Sublingual immunotherapy persistence and adherence in real‐world settings: A systematic review
Bibliographic record
Abstract
BACKGROUND: Sublingual immunotherapy (SLIT) adherence in the literature is often evaluated in closely monitored trials that may impact patient behavior; real-world SLIT adherence is relatively unknown. This systematic review intends to assess SLIT adherence in studies that reflect real-world settings. METHODS: A literature search of PubMed, Embase, Cochrane, Web of Science, and Scopus for real-world studies examining SLIT adherence was performed. Monitored clinical trials were excluded. Paired investigators independently reviewed all articles. For this review, "persistence" was defined as continuing therapy and not being lost to follow-up and "adherence" as persistence in accordance with prescribed SLIT dose, dosing schedule, and duration. Article quality was assessed using a modified Newcastle-Ottawa scale and then converted to AHRQ standards (good, fair, and poor). RESULTS: The search yielded 1596 nonduplicate abstracts, from which 32 articles (n = 63,683 patients) met criteria. Twenty-six (81%) studies reported persistence rates ranging from 7.0% to 88.7%, and 18 (56%) reported adherence rates ranging from 9.6% to 97.0%. Twenty-one (66%) studies surveyed reasons for discontinuing SLIT. All studies were Oxford level of evidence 2b and of good (n = 12) to fair (n = 20) quality. CONCLUSION: Reported rates of real-world SLIT persistence and adherence varied widely by study methodology (e.g., follow-up duration, objective vs. subjective assessment). Studies with longer follow-up generally reported lower rates; 3-year persistence ranged from 7% to 59.0% and 3-year adherence from 9.6% to 49.0%. Future studies of SLIT adherence would benefit from following concordant definitions of persistence/adherence and standardized reporting metrics.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".