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613 Underrepresentation of Minority Patients in an Observational Cohort Study

2022· article· en· W4313532832 on OpenAlexaff
Christine Peschken, David Robinson, Hani El‐Gabalawy, Konstantin Jilkine, Annaliese Tisseverasinghe, Carol Hitchon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineObservational studyCohortSystemic lupus erythematosusEthnic groupPopulationDemographicsRetrospective cohort studyCohort studyInternal medicinePediatricsDiseaseDemography

Abstract

fetched live from OpenAlex

Background Underrepresentation of ethnic minorities in lupus clinical trials has been identified as an important disparity. We aimed to compare our longitudinal observational lupus cohort participants to our entire lupus clinic population to see if similar disparities exist. Methods All patients seen at our academic centre were entered into a custom database from 1990 until 2015. Diagnoses, demographics, and disease manifestations were recorded. In 2015 this was supplanted by an electronic health record (EHR). In 2002, our centre began enrolling in a longitudinal observational research cohort; all patients meeting 1997 ACR criteria for SLE were eligible. Participation requires formal written consent. All patients with a diagnosis of SLE were abstracted from the database and EHR; only those seen after 2002, when cohort enrolment began, were included in this analysis. Demographics including ethnicity, age at onset, and disease duration and clinical manifestations of SLE were compared between Cohort (Co) and non-Cohort (non-Co) patients. Results 1236 patients were identified; 404 patients were excluded as there were no clinic visits after 2002. Of the remaining 832 patients, 349 (42%) were enrolled in the research cohort, 483 (58%) were not. Age at diagnosis was similar; (Co = 34±14 years vs. non-Co = 36±14 years, p=0.11), while disease duration at last follow-up was longer in Co patients (Co = 17±11 years vs. non-Co = 13±10 years, p<0.001). The ethnic distribution differed between the two groups. Co: White, n= 245 (70.2%); Indigenous n= 58 (16.6%); Asian n = 33 (9.5%); Other n= 13 (3.7%) vs non-Co: White, n= 252 (52.2%); Indigenous n= 162 (33.5%); Asian n = 57 (11.8%); Other n= 12 (2.5%); p<0.001. (figure 1A). Sex distribution was similar: (Co n=29 (8.3%); non-Co n=55 (11.4%), p=0.146). The proportion of patients who had died was higher in non-cohort patients, (Co n= 60 (17.2%); non-Co n= 119 (24.6%), p=0.01); and non-cohort patients were more likely to have died before the age of 50 (Co n=14 (23.3%); non-Co n=44 (37.0%), p=0.07) (figure 1A). Clinical manifestations are shown in figure 1B. While minor mucocutaneous manifestations were more frequent in Co patients, (Malar rash: Co n= 210 (60%); non-Co n= 193 (40%), p<0.001; Photosensitivity: Co n=152 (44%); non-Co n= 146 (30%), p<0.001); Mucosal Ulcerations: Co n= 197 (56%); non-Co n= 129 (28%), p<0.001) there was no difference in renal (Co n= 256 (53%); non-Co n=182 (52%), p=0.81), or neurologic involvement (Co n= 46 (10%); non-Co n= 50 (13%), p=0.21). Conclusions In this single academic centre study, ethnic minority patients were underrepresented in the observational research cohort, mirroring what is described in clinical trial participation. While disease severity (represented by renal and neurologic involvement) did not appear to differ, the higher death rate, and death rate at an early age among nonparticipants suggests underrepresentation of high-risk vulnerable patients in our observational cohort. Observational cohorts represent an important source of real-world data; without representative participation we are lacking data on those lupus patients with the highest prevalence and worst outcomes. Better engagement of ethnic minority and vulnerable patients in research will be key to improve understanding of lupus.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.145
GPT teacher head0.402
Teacher spread0.257 · 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.

Study designObservational
DomainMethods
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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