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Record W2735853984 · doi:10.1182/blood.v128.22.842.842

Inferior Access to Allogeneic Transplant in Disadvantaged Populations: A CIBMTR Analysis

2016· article· en· W2735853984 on OpenAlexaff
Kristjan Paulson, Ruta Brazauskas, Naya He, David Szwajcer, Matthew D. Seftel, Yoshiko Atsuta, Jignesh Dalal, Theresa Hahn, Carmem Bonfim, Nandita Khera, Linda J. Burns, Lih‐Wen Mau, Navneet S. Majhail, Wael Saber

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsMedicineTransplantationDemographyPoisson regressionDisadvantagedEpidemiologyPopulationInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Introduction: Allogeneic stem cell transplant is an intensive procedure, offered in a limited number of medical centres. We sought to describe how sociodemographic variables impacted access to transplant across the United States, and if disadvantaged populations had inferior access to transplant. Methods: Data from the Surveillance, Epidemiology and End Results Program (SEER) and the Center for International Blood and Marrow Transplant Research (CIBMTR) was integrated to determine the rate of unrelated donor transplantation between 2000 and 2010 in each of the 612 counties included in the SEER registry. Patients under the age of 65 with AML, ALL, and MDS were included, and the analysis was restricted to unrelated donors due to limited availability of ZIP code in CIBMTR data. New incident cases were determined from SEER, and the number of transplants was determined from CIBMTR. The transplant rate was calculated (transplants performed divided by incident cases) for each county. County attributes (percent minority, rural/urban status, percentage below the poverty line, and median family size) were obtained from US Census data. Poisson regression was used to describe how county attributes impacted transplant rates. Transplant rates were calculated separately for AML, White residents, and pediatric ALL. Results: 3147 patients were identified in the CIBMTR dataset that met inclusion criteria. The estimated ZIP code completeness was 75%. There were 30,468 new incident diagnoses of ALL, AML, and MDS. For AML, patients from rural areas (less than 20,000 residents) and patients from areas with higher poverty levels had lower transplant rates (Table 1). Minority status and family size did not impact transplant rate. In regression models, higher levels of poverty remained associated with lower transplant rates, while rurality did not (Table 2). The results were similar among White residents. In contrast, in pediatric ALL, no county attributes (poverty, rurality, percent minority, and family size) were significantly associated with a difference in transplant rate (Table 1). However, numbers of transplants were smaller, limiting power. Discussion: Patients with AML from disadvantaged areas had lower rates of unrelated donor transplant. While patients from disadvantaged areas were also more likely to be non-White, and non-White Americans are less likely to have an available unrelated donor, this difference was also seen in White Americans from disadvantaged areas. This suggests the lower transplant rate is due impaired access to transplant. Poverty rate was the most important predictor of transplant rate. The results of this study suggest that improving access to transplant in disadvantaged populations should be a priority for health care administrators. Based on these results, approximately 2500 Americans do not undergo allogeneic transplant annually due to inferior access associated with higher poverty rates. Table 1 Univariate Analysis Table 1. Univariate Analysis Table 2 Acute Myeloid Leukemia, Regression Model * Metropolitan = county > 50,000 people, micropolitan = county > 20,000, rural county < 20,000. Table 2. Acute Myeloid Leukemia, Regression Model. / * Metropolitan = county > 50,000 people, micropolitan = county > 20,000, rural county < 20,000. Disclosures Hahn: Novartis: Equity Ownership; NIH: Research Funding.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.356
Teacher spread0.309 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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