The case for development of a core outcome set (COS) and supplemental reporting guidelines for influenza vaccine challenge trial research in swine
Bibliographic record
Abstract
Previously, we systematically reviewed more than 20 years of influenza vaccine challenge trial research in pigs to answer the question, "does vaccinating sows protect offspring?" Overall, most studies were well designed but clinical heterogeneity made between-study comparisons challenging. Studies varied by samples, outcomes, and assays selected for measurement. Additionally, data essential for inclusion of findings in meta-analyses were often insufficiently reported and as a result, summary effect measures were either not derived or were not meaningful. Clinical heterogeneity and reporting issues complicate and limit what can be learned cumulatively from research and both represent two types of avoidable research waste. Here, we illustrate each concern using data collected tangentially during the systematic review and propose two corrective strategies, both of which have broad applicability across veterinary intervention research; (i) develop a Core Outcome Set (COS) to reduce unnecessary clinical heterogeneity in future research and (ii) encourage funders and journal editors to require submitted research protocols and manuscripts adhere to established reporting guidelines. As a reporting corollary, we developed a supplemental checklist specific to influenza vaccine challenge trial research in swine and propose that it is completed by researchers and included with all study protocol and manuscript submissions. The checklist serves two purposes: as a reminder of details essential to report for inclusion of findings in meta-analyses and sub-group meta-analyses (e.g., antigenic or genomic descriptions of influenza vaccine and challenge viruses), and as an aid to help synthesis researchers fully characterize and comprehensively include studies in reviews.
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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 | 0.840 | 0.916 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.015 | 0.017 |
| Research integrity | 0.020 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".