The Utilization of Splenectomy Post-Op Clinical Vaccinations Order Set to Enhance Adherence and Timeliness of Vaccinations in Emergency Splenectomy Patients: A Pre-and-Post Intervention Study
Bibliographic record
Abstract
Background: Asplenic patients are at increased risk of serious and life-threatening infections, especially by encapsulated pathogens. These infections are easily prevented with appropriate vaccinations post-splenectomy. The Splenectomy Post-Op Clinical Vaccinations Order Set is one of the first initiatives to harmonized practice between sites at our organization. Objectives: This is a pre and post-intervention study aimed to assess the impact of the Splenectomy Order Set to improve practice adherence to recommended vaccination protocols outlined by the Canadian Immunization Guidelines among patients undergoing emergency splenectomy. Methods: A 5-year retrospective chart review was conducted in 2013 for the pre-intervention vaccination rates. Another 5-year retrospective chart review was conducted in 2018 to determine post-intervention rates. The quantitative results were then tallied and analyzed. Results: Overall, 46 patients were included in the pre-intervention group and 40 patients in the post-intervention group. Prior to the implementation of the Splenectomy Post-Op Clinical Vaccinations Order Set, 61% of patients at the organization received the required vaccinations. Post-intervention, vaccination rates increased to 93%. There is a statistically significant difference between the two groups with a p value less than 0.01. Similarly, in terms of appropriate timing, pre-intervention, 48% of patients received the vaccines in the right timeframe and post-intervention, this rate increased to 80%. This difference did not reach statistical significantly due to a smaller sample size. Conclusion: This quality improvement study showed that a multidisciplinary and evidence-based order set can significantly improve appropriate drug prescribing and patient care while sustaining improved outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".