P-9. Standardized Model for SARS-CoV-2 and Influenza Vaccination of Hospitalized Patients: A Before-After Quality Improvement Study
Bibliographic record
Abstract
Abstract Background Hospitalized patients are at increased risk of complications from viral respiratory infection. Seasonal vaccination can reduce this risk and may be missed in the community for patients with prolonged hospital admissions. The optimal approach to ensuring timely vaccination of eligible inpatients against both seasonal influenza and SARS-CoV-2 has yet to be established. Methods We implemented a before-after quasi-experimental study of a standardized model for vaccinating patients admitted to an acute care hospital. The target population was inpatients designated as alternate level of care (ALC), defined as those recovered from their acute medical illness and awaiting transfer to post-acute care facility. The model involved an automated daily report of ALC patients without documented receipt of seasonal influenza or SARS-CoV-2 vaccine. This report was communicated daily by Infection Preventionists to unit-level pharmacist, physician, and unit leads to assess, consent and administer vaccination as appropriate. Vaccination rates of baseline (2022-23) and intervention (2023-24) seasons were measured and compared between identical time periods (10 Oct to 19 Jan). The primary outcome was the proportion of ALC patients vaccinated upon discharge, broken down by hospital versus community-initiated administration. Results At baseline, hospital-initiated vaccine accounted for 3.4% and 2% of vaccines in eligible patients against influenza and SARS-CoV-2, respectively. Post intervention, rates of hospital-initiated vaccination increase significantly for influenza (12%, OR 3.88, 95% CI 2.48-6.09; p< 0.001) and SARS-CoV-2 (10.7%, OR 5.76, 95% CI 3.30-10.04; p< 0.001). For influenza and SARS-CoV-2 vaccination during the intervention season, hospital-initiated vaccination contributed 76.3% and 78.4% of vaccination rate in Oct-Nov, as compared to 33.7% and 29.4% in Dec-Jan. Conclusion A standardized model resulted in significant uptake in vaccination against both influenza and SARS-CoV-2 during the 2023-24 respiratory viral season in ALC patients. The greatest impact occurred early in the season, likely due to slower uptake in the community. Further study is needed to assess the impact of this intervention on inpatient outcomes. Disclosures All Authors: No reported disclosures
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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 | 0.124 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".