MétaCan
Menu
Back to cohort
Record W4403500521 · doi:10.1016/j.xagr.2024.100412

Uterine fibroids with heavy menstrual bleeding stratified by race in a commercial and Medicaid database

2024· article· en· W4403500521 on OpenAlexaff
Sanjay K. Agarwal, Michael Stokes, Rong Chen, Cassandra Lickert

Bibliographic record

VenueAJOG Global Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicUterine Myomas and Treatments
Canadian institutionsCégep de Saint-Laurent
FundersMyovant Sciences
KeywordsMedicaidMenstrual bleedingRace (biology)Uterine fibroidsMedicineGynecologyObstetricsDatabaseGender studiesPolitical scienceHealth careComputer scienceSociology

Abstract

fetched live from OpenAlex

Background: Historically, the clinical characteristics and treatment pathways for patients with uterine fibroids and heavy menstrual bleeding have differed between White and Black women. Objective: To provide a contemporary comparison of patient characteristics and treatment patterns among White and Black women with uterine fibroids and heavy menstrual bleeding in the United States. Study Design: This retrospective cohort study included administrative claims data from 46,139 White and 17,297 Black women with uterine fibroids and heavy menstrual bleeding from the Optum Clinformatics database (January 2011-December 2020) and 7353 White and 16,776 Black women from the IBM MarketScan Multi-State Medicaid Insurance database (January 2010-December 2019). Patients were indexed at their initial uterine fibroid diagnosis claim and were required to have a claim for heavy menstrual bleeding and ≥12 months of continuous enrollment pre- and postindex. Patients were followed until the earliest of death, disenrollment, hysterectomy date, or end of study database. Outcomes were stratified by race and included patient demographics, clinical characteristics, pharmacologic treatment patterns, and surgeries/procedures. Pearson's Chi-square test for categorical variables and Student's t-test for continuous data were used to evaluate differences in baseline characteristics. Descriptive statistics were used to characterize treatment pathways for hormonal contraceptive use in women with ≥24 months of follow-up. Kaplan-Meier survival analysis was used to estimate time until hysterectomy, with log-rank testing to assess between-group differences. Results: <.0001). Approximately 40% of all patients received hormonal drug therapies as initial treatment, most commonly hormonal contraceptives. However, discontinuation of hormonal contraceptive therapy was nearly universal, with one-half discontinuing within a median treatment duration of ∼5 months. Most women stopped treatment after 1 or 2 agents (commercial: White, 89.9% [9757/10,857]; Black, 90.0% [3594/3993]; Medicaid: White, 92.2% [1635/1773]; Black, 94.2% [4454/4726]). Hysterectomy was the most common procedure, and was more common among White vs Black women (commercial: 43.9% [20,235/46,139] vs 37.8% [6536/17,297]; Medicaid: 46.8% [3444/7353] vs 32.0% [5364/16,776]). Conclusions: Black women with UF-HMB were diagnosed at a younger age than White women, and White women had higher hysterectomy rates than Black women, representing a shift from earlier researched treatment patterns. Patients with UF-HMB were also highly reliant on hormonal contraceptives, followed by nearly universal therapeutic discontinuation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.307
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAJOG Global ReportsSame topicUterine Myomas and TreatmentsFrench-language works237,207