Effects of socioeconomic status on enrollment in clinical trials for cancer: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To achieve equitable access to cancer clinical trials (CCTs), patients must overcome structural, clinical, and attitudinal barriers to trial enrollment. The goal of this systematic review was to study the relationship between socioeconomic status (SES), assessed either by direct or proxy measures, and CCT enrollment. METHODS: The review team and medical librarian developed search strategies for each database to identify studies for this systematic review, which was conducted according to PRISMA guidelines. Inclusion criteria were as follows: studies published in relevant scientific journals between January 2000 and July 2022, primary sources, English literature, and studies conducted in the US. Sixteen studies fulfilled the inclusion criteria and were reviewed. The risk of bias assessment was conducted independently by two reviewers using the Newcastle Ottawa scale. RESULTS: The initial search yielded 4070 citations, and 16 studies were included in our review. Four of the studies included used patient reported annual income as a measure of SES, while the remaining 12 studies used patient zip code as a proxy measurement of SES. Consistent with our hypothesis, 13 studies showed a positive association between high SES (patient-reported or proxy measurement) and CCT enrollment. Two studies showed a negative association, and one study showed no relationship. CONCLUSIONS: The existing literature suggests that low SES is associated with lower participation in CCT. The small number of studies identified on this topic highlights the need for additional research on SES and other barriers to CCT participation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.022 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it