Implementing a Nurse-Driven Protocol for Pneumococcal Vaccination in an Academic Rheumatology Clinic
Bibliographic record
Abstract
OBJECTIVE: Rheumatology patients are at high risk for complications from pneumococcal infections. The goal of this study was to assess the feasibility of implementing a nurse-driven pneumococcal vaccination protocol based on the 2012 Advisory Committee on Immunization Practices (ACIP) guidelines within an academic rheumatology clinic. Our aims were to increase (1) pneumococcal conjugate vaccine (PCV13) and pneumococcal polysaccharide vaccine (PPSV23) monthly vaccination rates in immunosuppressed patients aged 19 to 64 years, and (2) the overall proportion of immunosuppressed patients aged 19 to 64 years who have received both PCV13 and PPSV23 vaccinations by ≥ 10% over a 2-year period. METHODS: We identified eligible adults in the electronic medical record using a search protocol based on preset medication group. We obtained baseline pneumococcal vaccination rates in 2019, calculating the proportion of patients who were unvaccinated, partially vaccinated (received either PCV13 or PPSV23), or fully vaccinated. We created a pneumococcal vaccination protocol based on 2012 ACIP guidelines and converted it into a standing medical order to be implemented by the nursing staff. Postintervention vaccination rates were calculated monthly and at the end of the study period. Multiple comparison testing was performed to assess for significant postintervention changes. RESULTS: The average rate of monthly vaccination with either PCV13 or PPSV23 increased from 4.3% in 2019 to 12.6% in 2021. The proportion of patients who were fully vaccinated increased from 14.6% in 2019 to 26.2% in 2021. Both changes were statistically significant. CONCLUSION: It is feasible to employ a nurse-driven protocol for improving pneumococcal vaccination rates in immunosuppressed patients, despite difficulties posed by coronavirus disease 2019 (COVID-19) pandemic disruptions.
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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 | 0.110 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".