Screening Intervention to Identify Eligible Patients and Improve Accrual to Phase II-IV Oncology Clinical Trials
Bibliographic record
Abstract
PURPOSE: Low enrolment rates in clinical trials present a barrier to the development of novel cancer therapies. Currently, only 3% of patients with cancer participate, and many studies fail to achieve necessary enrolment. The objective of this study was to evaluate whether a screening intervention to identify potentially eligible patients (PEPs) would increase accrual rates. PATIENTS AND METHODS: Over a 4-month intervention period, PEPs for 21 phase II-IV breast, gastrointestinal, genitourinary, gynecology, and lung cancer trials were identified by a screening coordinator. This individual reviewed the electronic medical records of patients attending outpatient clinics and flagged PEPs for 10 medical oncologists at the BC Cancer Agency. Patients who were already documented to be trial eligible by physicians were not flagged. Oncologists were surveyed regarding the helpfulness and accuracy of the intervention. RESULTS: During the intervention period, 73 patients were enrolled, compared with 61 patients enrolled in the 4 months prior and 51 patients in the 4 months after. A total of 2,098 charts were reviewed, and 120 PEPs were identified during the intervention period, resulting in 19 PEPs who enrolled and four PEPs who declined a clinical trial. Relative accrual rates adjusted for oncologist appointments were 0.85 (P = .15) before and 0.70 (P < .005) after, relative to the intervention period. Oncologist-returned surveys indicated that 67% of flags were helpful, and 70% were accurate. CONCLUSIONS: In this study, manually screening patient records increased enrolment to specific clinical trials. A screening intervention process, involving a dedicated screening coordinator, should be considered to improve clinical trial accrual.
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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 | 0.060 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".