Poster 2000: A case study in effective innovative strategic multi-modal recruitment strategies in difficult-to-recruit patient populations: study of safety and tolerability of novel SLIT in an adolescent population
Bibliographic record
Abstract
This article was originally published online on 03 February 2014 Poor subject recruitment is a major cause of cost over-runs, extended finish dates, and delayed agency submissions. Failure to meet recruitment targets is particularly common in allergy immunotherapy studies where unique challenges such as allergen seasonality, lengthy study durations and studying allergen specific populations are necessary. Historically, standard modalities of advertising in newspaper, radio, and television have been employed with mixed success. Increasing availability of communication and sharing technologies, and social media availabilities, affords innovative approaches and tools to customize the recruitment of subjects, especially difficult populations. A dose escalation study investigated the safety and tolerability of a novel sub-lingual immunotherapy in subjects aged 12-17yrs old for the treatment of house dust-mite allergy (HDM). Several challenges to the recruitment of adolescents were overcome including: tight study inclusion criteria, summer vacations, conflicting parent & adolescent schedules, confounding seasonal allergens, and the beginning of a new school year within two months from the study start-up. After thorough identification of HDM allergy symptoms, an appropriate advertising message was created and approved by the sponsor and IRB for use in all advertising campaigns and modalities. A cutting-edge model has been developed with a Business-To-Consumer (B2C) marketing mindset. A similar model to our proprietary Online Central Recruitment Update Platform was used where ideal subject profiles are constructed and advertising avenues are specifically chosen to ensure high penetration into the target demographic. A mix of traditional plus B2C advertising strategies led to successful recruitment mid-September 2013 and on schedule database soft-lock. 160 subjects were booked for screening, 129 screened, 37 screen-passed and dosed, and 36 completed the study with 35 per protocol completes. The mean age of the subjects passing all inclusion/exclusion criteria and completing the study was 15.3±0.5; male to female ratio was 3:2. Strategic planning of marketing and advertising campaigns using a blend of carefully selected traditional and contemporary modalities, targeted to a well-defined niche demographic, yielded successful recruitment of an HDM allergic adolescent population.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".