An asynchronous electronic consent for improving consent in research among patients with cancer
Bibliographic record
Abstract
BACKGROUND: Informed consent is essential to ensuring ethical conduct of clinical research in oncology, but can be challenging and time-consuming to implement. Electronic consenting (e-consent) may address these issues, but acceptability of e-consent among patients with cancer must be assessed. METHODS: An asynchronous preliminary e-consent was developed for a prospective circulating tumor DNA testing study for patients with colorectal/pancreatic cancer. Following e-consent, patient satisfaction was assessed in a follow-up call before full consent was obtained. Acceptability was assessed with descriptive statistics and bivariate analysis. RESULTS: Fifty-one participants completed the preliminary e-consent, 90% of which preferred electronic full consent over traditional consent. Comfort enrolling after e-consent was rated highly by 93% of participants. Additionally, 80% of participants indicated that a follow-up call had no impact on decision to enroll. CONCLUSION: These findings suggest that asynchronous e-consenting is highly acceptable among oncology patients but future work should assess comprehension with this approach.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".