Quality versus risk of bias assessment of palliative care trials: comparison of two tools
Bibliographic record
Abstract
BACKGROUND: Randomised controlled trials (RCTs) of palliative care interventions are challenging to conduct and evaluate. Tools used to judge the quality of RCTs do not account for the complexities of conducting research in seriously ill populations and may artificially downgrade confidence in palliative care research. OBJECTIVE: To compare assessments from the Palliative Care Trial Assessment Tool (PCTAT) and Cochrane Risk of Bias (RoB) tool. DESIGN: )) and examined (dis)agreement for comparable items. We assessed quality of life at 1-3 months among trials grouped according to RoB or PCTAT score (using meta-analysis) and whether RoB or quality improved over time (Cochran-Armitage trend test). RESULTS: was 0.33 (95% CI 0.19 to 0.42). Total agreement between comparable items was observed for 56% of trials (24/43) and total disagreement for 21% (8/43). The standardised mean difference in quality of life was statistically significant among RCTs with low RoB and high PCTAT, but not for those with medium/low PCTAT or high/unclear RoB. Quality of reporting improved over time, whereas RoB did not. CONCLUSION: Although there was fair agreement between tools, areas of disagreement/non-comparability suggest the tools capture different aspects of bias/quality. A specific tool to evaluate quality of palliative care trials may be warranted.
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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 | 0.666 | 0.874 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.036 | 0.045 |
| Bibliometrics | 0.046 | 0.028 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".