Translational medicine related tools for quality assessments of clinical trials and the trial reports
Bibliographic record
Abstract
Objective and fair clinical trials are the main methods for assessing the clinical significances of the experimental findings. The development of translational medicine highly relies on high-quality clinical trials as well as trial reports. Although the definition ofquality of clinical trialsandquality of trial reportsdiffers from each other, they are closely related and can be consistent in most circumstance in the context ofscientific integrity. The quality of trial reports can be basically assessed by their internal and external properties. The quality of a randomized trial can be assessed by Jadad scale and Cochrane collaboration's tool for assessing risk of bias, and the quality of a non-randomized trial by risk of bias tool and Newcastle-Ottawa scale. However, since Jadad scale lacks appropriate appraisal of allocation concealment and is too simple in evaluating blind method, assessment of allocation concealment should be added. A more widely accepted approach for assessing the quality of random trials is the combination of Jadad scale and Schulz's approach to allocation concealment till recent years.For non-randomized cohort studies and case-control studies, Newcastle-Ottawa scale might be suitable at present time. Key words: Translational medicine; Quality assessment of clinical trials; Quality assessment of trial reports; Jadad scale; Schulz approach to allocation concealment; Risk of bias tool; Newcastle-Ottawa scale
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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 | 0.550 | 0.810 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.045 | 0.040 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.050 | 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".