Strategies for successful rapid trials of influenza vaccine
Bibliographic record
Abstract
BACKGROUND: In contrast to the gradual pace of conventional vaccine trials, evaluation of influenza vaccines often must be accelerated for use in a pandemic or for annual re-licensure. Descriptions of how best to design studies for rapid completion are few. PURPOSE: In August, 2010, we conducted a rapid trial with a seasonal influenza vaccine for 2010-2011 given to persons vaccinated with an adjuvanted H1N1 vaccine in 2009, to determine whether re-exposure to the H1N1(2009) component of the seasonal vaccine would cause increased reactions. We describe the strategies that we believe were responsible for success in meeting the desired timeline. METHODS: The key means for expediting the study were: use of a few experienced, well-staffed centers; efficient completion of administrative approvals; advance recruitment of volunteers; synchronized start among centers with rapid completion (≤1 week) of first visits; rapid data assembly via the Internet; and a well-prepared data analysis plan. We chose to use a randomized, blinded, cross-over design to allow estimation of vaccine-attributable adverse event rates, with sufficient power (320 participants) to detect events occurring at true rates ≥1% with ≥90% probability. RESULTS: Planned enrollment numbers, center synchronization, and timelines, including review by a safety board prior to the cross-over step (second doses), were achieved. A detailed safety report was delivered to federal health officials just 32 days after study initiation and was used to fine-tune public messaging prior to the mass vaccination programs across Canada. LIMITATIONS: This aggressive timeline could not have been met without opportunities for careful planning and the prior existence of a network of experienced, collaborating trial centers. CONCLUSIONS: The means used to accelerate this study timeline were successful and could be used in other urgent situations but the mechanics of collaborative trials must be well rehearsed as a precondition.
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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 | 0.314 | 0.232 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".