Availability of data for screening, offering, and consenting patients to cancer clinical trials: Report from an ASCO-ACCC collaboration.
Bibliographic record
Abstract
6530 Background: Only a small fraction of patients with cancer participate in treatment trials. Patients identifying as members of racial and ethnic minority groups are consistently underrepresented in these trials. A recent systematic review reported that patients, regardless of race and ethnicity, are willing to enroll in trials if asked to participate by their treating clinician. Prospective and longitudinal data and metrics at the site- and clinician-level are necessary to understand whether patients are equitably considered for clinical trials. Methods: ASCO and Association of Community Cancer Centers (ACCC) developed a self-assessment for trial sites to record and gauge the number of patients across races and ethnicities screened, offered, and enrolled into clinical trials. Research sites, from across the US, were recruited through an open call to apply to participate in the ASCO-ACCC Pilot Project. There were 65 sites assigned to this pilot study, which tested the feasibility and utility of the site assessment. Sites were asked to enter 2019 and 2020 aggregate data for each step along the clinical trial enrollment continuum by select races and ethnicities (Black, Hispanic/Latinx, White) and overall. Results: 62 of 65 sites completed the study and represented a range of settings and practice types (61% academic, 26% hospital/health system, 13% independent). Only 2 sites (3%) were able to provide the data requested at each enrollment step in the assessment (table). Sites that collected the data did not do so routinely (table) and most had to compile data through multiple sources and/or manual extraction (40-100% across enrollment steps). Sites with missing data reported they did not collect data at all (36-64% across enrollment steps), did not collect data in a systematic way (0-29% across enrollment steps), or stated it would be too burdensome to manually review charts to extract data (12-29% across enrollment steps). Conclusions: Data collection and routine evaluation of participation metrics, by race and ethnicity, are necessary to assess and monitor equity and diversity in clinical trials. Most sites in this study did not collect, or routinely collect, data for screening, offering, and consenting patients to clinical trials. Without these data, sites are unable to evaluate and monitor whether their patients have equitable access to clinical trials or establish strategies to address any inequities. ASCO and ACCC will continue to partner with sites to better understand their processes and the feasibility of collecting such data in a systematic and automated way, such as through electronic health record systems. [Table: see text]
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 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 | 0.293 | 0.486 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.030 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".