The Rethinking Clinical Trials (REaCT) Program. A Canadian-Led Pragmatic Trials Program: Strategies for Integrating Knowledge Users into Trial Design
Bibliographic record
Abstract
We reviewed patient and health care provider (HCP) surveys performed through the REaCT program. The REaCT team has performed 15 patient surveys (2298 respondents) and 13 HCP surveys (1033 respondents) that have addressed a broad range of topics in breast cancer management. Over time, the proportion of surveys distributed by paper/regular mail has fallen, with electronic distribution now the norm. For the patient surveys, the median duration of the surveys was 3 months (IQR 2.5-7 months) and the median response rate was 84% (IQR 80-91.7%). For the HCP surveys, the median survey duration was 3 months (IQR 1.75-4 months), and the median response rate, where available, was 28% (IQR 21.2-49%). The survey data have so far led to: 10 systematic reviews, 6 peer-reviewed grant applications and 19 clinical trials. Knowledge users should be an essential component of clinical research. The REaCT program has integrated surveys as a standard step of their trials process. The COVID-19 pandemic and reduced face-to-face interactions with patients in the clinic as well as the continued importance of social media highlight the need for alternative means of distributing and responding to surveys.
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 | 0.789 | 0.781 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".