Viral interference and the live-attenuated intranasal influenza vaccine: Results from a pediatric cohort with cystic fibrosis
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to explore the effects of viral co-detection in individuals recently vaccinated with the live-attenuated intranasal influenza virus vaccine (LAIV) on the detection of influenza RNA. METHODS: Before the 2013-2014 influenza season, nasal swabs were obtained from 59 pediatric participants with cystic fibrosis (CF) and 17 of their healthy siblings immediately before vaccination and 4 times during the week of follow-up. Real-time RT-PCR assays were used to detect influenza RNA. Co-detection of a non-influenza respiratory virus (NIRV) at the time of vaccination was determined by a multiplex RT-PCR assay. Differences in the proportions and rates of influenza detection and their 95% credible intervals (CrI) were estimated. RESULTS: Influenza RNA was detected in 16% fewer participants (95% CrI: -7, 39%) throughout follow-up in the NIRV-positive group compared with the NIRV-negative group (59% vs. 75%). This was also observed in participants with CF alone (66% vs. 74%; RD = 8% 95% CrI: -16, 33%) as well as in healthy participants only (75% vs. 30%; RD = 45%, 95% CrI: -2, 81%). Influenza was detected in NIRV-negative subjects for 0.49 d more compared with NIRV-positive subjects (95% CrI: -0.37, 1.26). CONCLUSION: The observed proportion of subjects in whom influenza RNA was detected and the duration of detection differed slightly between NIRV- positive and -negative subjects. However, wide credible intervals for the difference preclude definitive conclusions. If true, this observed association may be related to a recent viral respiratory infection, a phenomenon known as viral interference.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".