Randomization is not associated with socio-economic and demographic factors in a multi-center clinical trial of children with sickle cell anemia
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated factors influencing participation rates for minority children with a chronic disease in clinical trials. The Silent Cerebral Infarct Multi-Center Clinical (SIT) Trial provides an opportunity to study the impact of demographic and socio-economic factors on randomization in a clinical trial among Black children. Our primary objective was to characterize the factors associated with successful randomization of children with sickle cell disease (SCD) and silent cerebral infarct (SCI) in the SIT Trial after initial consent. PROCEDURE: Differences in socio-economic and demographic variables, family history and disease-related variables were determined between eligible participants who were successfully randomized and those who were not randomized following initial consent. Head of household educational level and family income were examined separately for US versus non-US sites. RESULTS: Of 1,176 children enrolled in the SIT Trial, 1,016 (86%) completed screening. Of 208 (20%) children with qualifying SCI on pre-randomization MRI, 196 (94%) were successfully randomized. There were no differences in socio-economic, demographic, or disease-related variables between children who were or were not randomized. Participants from non-US sites were more likely to be randomized (22% vs. 12%, P = 0.011); although, randomization by country was associated with neither head of household education nor family income. CONCLUSION: In the SIT Trial, acceptance of random allocation was not associated with socio-economic or demographic factors. Although these factors may represent barriers for some participants, they should not bias investigators caring for children with SCD in their approach to recruitment for clinical trial participation.
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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 | 0.028 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".