Effectiveness of a screening intervention to identify clinical-trial-eligible patients.
Bibliographic record
Abstract
6069 Background: Advancements in cancer therapy require clinical trials, but only 3% of all cancer patients (CP) participate in trials causing many studies to be delayed or fail to complete. Literature indicates that accrual to clinical trials is primarily driven by MD related factors. The objective of this study was to evaluate whether the screening and identification of potentially eligible patients (PEP) for specific clinical trials would lead to an increased rate of accrual (RA). Methods: During a 4 month period, the charts of CP attending the outpatient clinics of 12 MDs were reviewed to determine eligibility for 21 phase II-IV trials for CP with BR, GI, GU, GY, or LG cancer. Trials were included if they had been open for ≥ 4 months and would remain open ≥ 8 months from the start of the intervention. A screening coordinator with minimal clinical background reviewed the electronic record of new and followup CP to determine eligibility according to protocol specified criteria. PEP were identified for medical oncologist by attaching notices to CP charts. Participating MDs were surveyed regarding the helpfulness and accuracy of the forms. A negative-binomial regression model was used to compare RA and find 95% CI for relative rates. Results: Between May 1 to August 31, 2011 a total of 2,098 charts were screened for eligibility for 21 trials, and 120 PEP were identified. Of these, 15 were randomized to the referred study, 4 to a different study, and 4 CP were offered but declined the referred study. Four month RA for included trials were 61 before, 73 during and 51 after the intervention. Relative rates adjusted for MD bookings were 0.85 (95% CI: 0.67, 1.06, p = 0.15) before and 0.70 (95% CI: 0.54, 0.90, p < 0.005) after, relative to during the intervention. 33 completed questionnaires were received: 22 (67%) were helpful and 23 (70%) were accurate. Screening required a 1.0 Full Time Equivalent position during the period of the intervention. Conclusions: Manual screening of patient records to determine clinical trial eligibility is labor intensive and increases enrolment to specific clinical trials. Notifications were deemed mostly helpful and accurate by oncologists. Screening interventions 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 | 0.020 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".