Quality of Reporting of Modern Randomized Controlled Trials in Medical Oncology: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The Consolidated Standards of Reporting Trials (CONSORT) guidelines were developed in the mid-1990s for the explicit purpose of improving clinical trial reporting. However, there is little information regarding the adherence to CONSORT guidelines of recent publications of randomized controlled trials (RCTs) in oncology. METHODS: All phase III RCTs published between 2005 and 2009 were reviewed using an 18-point overall quality score for reporting based on the 2001 CONSORT statement. Multivariable linear regression was used to identify features associated with improved reporting quality. To provide baseline data for future evaluations of reporting quality, RCTs were also assessed according to the 2010 revised CONSORT statement. All statistical tests were two-sided. RESULTS: A total of 357 RCTs were reviewed. The mean 2001 overall quality score was 13.4 on a scale of 0-18, whereas the mean 2010 overall quality score was 19.3 on a scale of 0-27. The overall RCT reporting quality score improved by 0.21 points per year from 2005 to 2009. Poorly reported items included method used to generate the random allocation (adequately reported in 29% of trials), whether and how blinding was applied (41%), method of allocation concealment (51%), and participant flow (59%). High impact factor (IF, P = .003), recent publication date (P = .008), and geographic origin of RCTs (P = .003) were independent factors statistically significantly associated with higher reporting quality in a multivariable regression model. Sample size, tumor type, and positivity of trial results were not associated with higher reporting quality, whereas funding source and treatment type had a borderline statistically significant impact. CONCLUSION: The results show that numerous items remained unreported for many trials. Thus, given the potential impact of poorly reported trials, oncology journals should require even stricter adherence to the CONSORT guidelines.
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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 | 0.497 | 0.740 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".