Does Modification to the Approach to Contacting Potential Participants Improve Recruitment to Clinical Trials?
Bibliographic record
Abstract
BACKGROUND: It is critical that clinical trial researchers ensure efficient and successful patient recruitment. Recruitment is often slower than expected and required sample sizes not obtained within initial funding deadlines. There is little rigorous evidence supporting ways to improve recruitment. We hypothesized making telephone contact with subjects prior to hospital attendance would improve recruitment rates into clinical trials. METHODS: Retrospective post hoc analysis of recruitment rates in an on-going clinical trial was undertaken. Two hundred twelve consecutive patients were recruited over 6 months. During the first 3 months, patients received a telephone call from the research team and also received an information sheet by post prior to clinic attendance (group 1). The study was discussed on telephone and any issues were re-addressed at the patient's clinic appointment when they were formally invited to participate in the study. After 3 months, the investigators stopped telephoning the patients (group 2); patients were invited to participate in the study by post and were first spoken to directly by an investigator in clinic. The study protocol and investigators did not change between groups. RESULTS: There was no significant difference in baseline demographics between the two groups. There was a significant improvement in recruitment rate in group 1 compared to group 2 (77.7% vs. 45.0%, P < 0.0001). An improvement in clinic attendance rate in group 1 was observed, although this was not significant (did not attend rate: 2.9% vs. 7.8%, P = 0.14). CONCLUSION: Telephone contact between researchers and potential participants prior to clinic attendance can greatly improve study recruitment rates. This information may benefit the design of all clinical studies.
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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 | 0.309 | 0.553 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".