Do SMS/e-mail reminders increase influenza vaccination of rheumatoid arthritis patients under anti-TNF: a nested randomized controlled trial in the ART e-cohort
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to evaluate the effectiveness of short message service (SMS) and/or email reminders in improving influenza vaccination coverage rates among RA patients treated with anti-TNF therapies, and to identify factors associated with vaccination. METHODS: This study was a nested randomized controlled trial in the ART e-cohort, an ongoing French nationwide multicentre prospective cohort of RA patients treated with anti-TNF therapy. Patients were 1:1 randomized, with stratification on age. The intervention consisted of regular reminders via SMS and/or emails to get vaccinated against influenza during the vaccination campaign. At the end, all participants received a questionnaire. The primary outcome was influenza vaccination coverage. Secondary outcomes included the vaccination coverage before and after the COVID-19 pandemic, and factors associated with vaccination. RESULTS: Between October 2021 and April 2022, 446 participants were randomized (224 to the intervention group and 222 to the control group). Among them, 325 (73%) reported their vaccination status and 221 (68%) were vaccinated against influenza: 116/158 (73%) in the intervention group, vs 105/167 (63%) in the control group (relative risk 1.08; 95% CI 0.95-1.23). The vaccination coverage before and after the COVID-19 pandemic did not differ (72% vs 72%; 95% CI -8% to 8%). Age ≥65 years [odds ratio (OR) 6.25; 95% CI 2.88-13.60] and previous influenza vaccination in the years before inclusion (OR 7.81; 95% CI 4.36-14.02) were associated with higher rates of vaccination. CONCLUSION: SMS and/or e-mail reminders did not significantly improve influenza vaccination rates in our cohort. The COVID-19 pandemic did not substantially impact the influenza vaccination coverage. Our results might be counterbalanced by an already high vaccination coverage. TRIAL REGISTRATION: ClinicalTrials.gov, http://clinicaltrials.gov, NCT05220423, NCT03062865.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".