Feasibility of a cluster-randomized influenza vaccination trial in U.S. nursing homes: Lessons learned
Bibliographic record
Abstract
Influenza severity increases and vaccine effectiveness decreases with age. High-dose influenza vaccine (HD) with quadruple the antigen of standard-dose (SD) vaccine is more efficacious in community-dwelling persons 65 years and older. We evaluated the feasibility of recruiting and randomizing Medicare certified nursing homes (NHs) for a pragmatic cluster-randomized trial comparing HD vs. SD (NCT1720277). Residents were long-stay and at least 65 years old. NH leadership agreed to standard of care random assignment with HD (Fluzone® High-Dose) or SD (Fluzone®) influenza vaccine for their facility for the 2012-2013 influenza season. We used Minimum Data Set (MDS) 3.0 and Vital Status records for pre-specified clinical outcomes: 1) all-cause hospitalization, 2) NH mortality, and 3) functional decline. Intent-to-treat analyses were performed at the resident-level using Cox proportional hazards, multivariable Poisson, and logistic regression models accounting for clustering by facility. We randomized 39 NHs (19 SD and 20 HD), coordinated vaccine delivery, implemented web-based data collection, and accessed MDS data, demonstrating feasibility. There were 2,957 eligible residents (SD 1496; HD 1461); characteristics were similar between groups. A total of 301 (20.1%) of SD and 197 (13.5%) of HD allocated residents were ever hospitalized, (adjusted relative risk 0.680; 95% CI: 0.537, 0.862; p = 0.001). NH mortality was 274 (18.3%) SD vs. 249 (17.1%) HD, adjusted relative risk 0.834; 95% CI: 0.678, 1.027; p = 0.087). There were no differences in decline in functional status (13.4 vs. 13.8%, adjusted relative risk 0.994; 95% CI: 0.774,1.278; p = 0.965). We demonstrate that a pragmatic large-scale trial is feasible in a NH setting.
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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 | 0.078 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".