Adherence and persistence in allergen immunotherapy (APAIT): A reporting checklist for retrospective studies
Bibliographic record
Abstract
BACKGROUND: Adherence is essential for the long-term efficacy of allergen immunotherapy (AIT) and has been evaluated in numerous retrospective studies. However, there are no published guidelines for best practice in measuring and reporting adherence or persistence to AIT, which has resulted in substantial heterogeneity among existing studies. The 'adherence and persistence in AIT (APAIT)' checklist has been developed to guide the reporting, design and interpretation of retrospective studies that evaluate adherence or persistence to AIT in clinical practice. METHODS: Five existing checklists, focussing on study protocol design, the use of retrospective databases/patient registries, and the appraisal and reporting of observational studies, were identified and merged. Relevant items were selected and tailored to be specific to AIT. The content of the checklist was discussed by 11 experts from Europe, the United States and Canada, representing allergy, healthcare and life sciences, and health technology appraisal. RESULTS: The APAIT checklist presents a set of items that should either be included or at least considered, when reporting retrospective studies that assess adherence or persistence to AIT. Items are organized into four categories comprising study objective, design and methods, data analysis, and results and discussion. The checklist highlights the need for clarity and transparency in reporting and emphasizes the importance of considering potential sources of bias in retrospective studies evaluating adherence or persistence to AIT. CONCLUSIONS: The APAIT checklist provides a pragmatic guide for reporting retrospective adherence and persistence studies in AIT. Importantly, it identifies potential sources of bias and discusses how these influence outcomes.
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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 | 0.519 | 0.662 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".