Feasibility of oncology clinical trial-embedded evaluation of social determinants of health
Bibliographic record
Abstract
Social determinants of health (SDoH) are associated with stark disparities in cancer outcomes, but systematic SDoH data collection is currently absent from oncology clinical trials. Trial-based SDoH data are essential to ensure representation of marginalized populations, contextualize outcomes, and identify health-equity intervention opportunities. We report the feasibility of the first pediatric oncology multicenter trial-embedded SDoH investigation. Among 448 trial participants, 392 (87.5%) opted-in to the embedded SDoH study; 375 (95.7%) completed baseline surveys, with high longitudinal response rates (87.2-92.8%) over 24-months of therapy. Trial-embedded SDoH data collection is feasible and acceptable, and must be consistently included within future oncology trials.
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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 | 0.310 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".