Top Ten Tips Palliative Care Clinicians Should Know About Designing a Clinical Trial in Palliative Care
Bibliographic record
Abstract
The palliative care field is experiencing substantive growth in clinical trial-based research. Randomized controlled trials provide the necessary rigor and conditions for assessing a treatment's efficacy in a controlled population. It is therefore important that a trial is meticulously designed from the outset to ensure the integrity of the ultimate results. In this article, our team discusses ten tips on clinical trial design drawn from collective experiences in the field. These ten tips cover a range of topics that can prove challenging in trial design, from developing initial methodologies to planning sample size and powering the trial, as well as collaboratively navigating the ethical issues of trial initiation and implementation as a cohesive team. We aim to help new researchers design sound trials and continue to grow the evidence base for our specialty. The guidance provided here can be used independently or in addition to the ten tips provided by this team in a separate article focused on what palliative care clinicians should know about interpreting a clinical trial.
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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 | 0.251 | 0.616 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.025 | 0.052 |
| Insufficient payload (model declined to judge) | 0.011 | 0.014 |
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".