An Electronic Information Kiosk for Enhancing Patient Accrual for Cancer Clinical Trials: A Pilot and Feasibility Study
Bibliographic record
Abstract
Introduction Low accrual to clinical trials for solid tumors at our institution led to a review of possible modifiable factors within our control. This led to a pilot project to determine whether improved patient awareness could alter accrual rates to active trials. Methods An information kiosk was located at the patient library on the ground floor of the London Regional Cancer Program. Adult cancer patients were invited to learn more about clinical trials from our research navigator, including specific trials open in our center, and to participate in the study, which involved a brief satisfaction and demographics survey. Results Three hundred and eighty-six (386) patients interacted with the clinical trial information kiosk over the eight weeks it was open. Of these, 32 patients consented and filled out surveys, which indicated an overall positive interaction with the kiosk. Unfortunately, in the time period examined, clinical trial accrual rates appeared to decrease when the pre- and post-kiosk activation periods were compared (44 versus 37 patients accrued to various trials). Conclusion Our pilot study found that the implementation of a clinical trial information kiosk was easy to understand and useful for patients to learn more about clinical trials. Barriers to this patient satisfaction translating into increased accrual rates in our center included suboptimal kiosk location and lack of guidance to the kiosk from clerical staff. High patient satisfaction scores support the potential value of permanent clinical trial information kiosks in our cancer center, but this requires increased attention to visibility, location, and staff education.
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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 | 0.041 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".