Immunization Coverage and Clinical Documentation of Vaccine Refusal in Pediatric Clinics: A Retrospective Analysis in the Midwestern United States, 2022-2024
Bibliographic record
Abstract
Objective: Vaccine hesitancy has increased in recent years, prompting some pediatric practices to implement strict immunization policies. Accurate documentation of vaccine refusal is essential for monitoring trends and informing interventions. This retrospective analysis assessed immunization coverage and refusal documentation among pediatric patients in a Midwestern health care system with a policy requiring adherence to the Centers for Disease Control and Prevention immunization schedule. Study design: Z codes to identify documentation of underimmunization and refusal. We used logistic regression to examine associations between refusal documentation and patient characteristics. Results: Among 2164 eligible patients, 300 (13.9%) had documented vaccine refusal. Coverage for most immunizations was comparable with national estimates. Refusal documentation was more common among White non-Hispanic patients and those with Medicaid or self-pay insurance. Patients from greater-income neighborhoods had greater odds of documented refusal. Among refusal codes, 39.6% used the designation "patient refusal," a code intended for individuals making their own health care decisions. Conclusions: codes to document refusal limited interpretability by not specifying the immunization refused or whether it was a true refusal or delay. Improved coding specificity and integration of refusal tracking into electronic health records may enhance the utility of clinical data for monitoring immunization trends and informing policy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".