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Record W4408418805 · doi:10.1177/00099228251324017

Electronic Health Record Clinical Decision Support to Close the Human Papillomavirus Vaccination Gender Disparity in Children Aged 9 and 10 Years

2025· article· en· W4408418805 on OpenAlexaff
Carole H. Stipelman, Erica Ulibarri, Natalie Wilson, Alexis R. Olivas, Elly Trepman, Kensaku Kawamoto

Bibliographic record

VenueClinical Pediatrics · 2025
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversity of Manitoba
FundersNational Center for Advancing Translational SciencesAmerican Cancer Society
KeywordsMedicineOutreachHuman papillomavirusVaccinationElectronic medical recordHealth recordsElectronic health recordFamily medicinePediatricsHealth careInternal medicineImmunology

Abstract

fetched live from OpenAlex

We performed a quality improvement project (31 clinics; July 2021 to October 2023) to increase human papillomavirus vaccination initiation frequency and decrease gender disparity in children aged 9 and 10 years. The 11 process changes included electronic health record clinical decision support (CDS) tools for providers, staff, and parents and medical assistant participation. In phase 1 (preparation), initiation frequency was lower in boys (250 of 1688 visits, 15%) than girls (289 of 1549 visits, 19%; P = .003). In phase 2 (CDS alerts; recommended initiation age lowered from 11 to 9 years), initiation frequency was increased and similar between boys (906 of 1847 visits, 49%) and girls (867 of 1740 visits, 50%; P = .64). In phase 3 (patient portal outreach), initiation frequency was increased further for boys and girls. The multifaceted intervention, including CDS tools and lowering the initiation age, was associated with increased initiation frequency and decreased gender disparity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.067
GPT teacher head0.479
Teacher spread0.412 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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