Analysis of the Transplantation and Cellular Therapy Health Equity Literature: Trends By Topic and Disparity over Time
Bibliographic record
Abstract
Introduction Health equity is increasingly being recognized as a critical priority across medicine, including in transplantation & cellular therapy (TCT). Herein we report the results of a scoping review of TCT health equity. We aimed to gather published articles together to highlight TCT health equity works, note areas that would benefit from more attention & inspire efforts to spearhead projects characterizing/addressing disparities. Methods We applied PRISMA-P extension guidelines to develop/publish a scoping review protocol, https://osf.io/exuzc. Articles were eligible if focused on TCT health equity & published in one of 22 hematology journals 1/1/18-3/13/24 or 46 oncology/medicine/pediatrics journals 1/1/18-4/4/24 which had an impact factor >9.2 or were listed in Clarivate's Journal Citation Reports first quartile or were a major hematology/oncology society's official journal. A research librarian crafted a search to retrieve all articles with ≥1 health equity subject heading or ≥1 title/abstract health equity keyword from a predefined list. Articles underwent title/abstract then full text review & data extraction by 2 independent team members. All extracted data were published to docs.google.com/spreadsheets/d/1rFeGrNVdIDnYEzL9KPB1rqyZUafWGcoN11FsKGUzyCY. Results The search returned 5,494 articles; 1,009 underwent full-text screening with 121 included in the review, Fig 1. Of these, 56 were published in BBMT / JTCT & 20 in BBMT, reflecting 2.3% & 0.9% of the proportions of all articles published in these journals during the review period. Most articles were about patients, with 17 (14%) about stem cell donors. 16 (13%) had a pediatric/adolescent focus. 68 (56%) were retrospective studies, with 19 (16%) prospective/trials & 1 studying basic science correlates of disparities. 24 (20%) described interventions to advance equity & 4 (3%) social determinants of health data collection process improvement. Of 95 research articles (ie excluding 26 editorials/reviews), nearly half (45/95, 47%) reported disparities in disease phenotype/outcomes, with 26/95 (27%) on access to care. Most articles focussed on race/ethnicity or socioeconomics, with only 2 (2%) on sexual/gender minorities, 3 religion, 1 incarceration, & none on disability, Fig 2. Only 2 noted partnerships with the populations impacted. Analysis over time shows an increasing number & proportion of TCT health equity articles, Fig 3. Conclusion Our analysis highlights recent progress characterizing TCT inequities & clarifies areas warranting further study. We also establish benchmarks for the proportion of health equity articles published. This review will serve as an important educational resource to advance equity, helping researchers & the field in general to assimilate the rapidly expanding TCT health equity literature.
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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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".