Implementing electronic patient reported outcome data capture for multi-centre oncology clinical trials
Bibliographic record
Abstract
Abstract Background:Traditionally patient reported outcomes (PRO) are collected on paper as key endpoints for clinical trials. With increasing internet usage there is an interest in the use of electronic patient reported outcomes. Although previous studies in the general oncology clinical setting have shown the equivalence of scores in paper and electronic formats, there is a wide ranging level of uptake of electronic patient reported outcome acceptance amongst trial patients.Implementation plan:We have chosen to implement the use of electronic patient reported outcomes in our clinical trial population using a study within a trial (SWAT) to assess functionality and acceptability to patients. This will be led by a multi-disciplinary team of project managers, methodologists, clinicians and data scientists. The implementation plan has multiple ethical and regulatory considerations required in system identification, patient and public involvement and the design of a SWAT. Conclusion: Implementation of ePRO even in a world of increasing technology use is a complex and multifactorial project requiring careful consideration and adequate resourcing.
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 | 0.644 | 0.668 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".