The quality of reporting of RCTs used within a postoperative pain management meta-analysis, using the CONSORT statement
Bibliographic record
Abstract
BACKGROUND: Randomized controlled trials (RCTs) are routinely used in systematic reviews and meta-analyses that help inform healthcare and policy decision making. The proper reporting of RCTs is important because it acts as a proxy for health care providers and researchers to appraise the quality of the methodology, conduct and analysis of an RCT. The aims of this study are to analyse the overall quality of reporting in 23 RCTs that were used in a meta-analysis by assessing 3 key methodological items, and to determine factors associated with high quality of reporting. It is hypothesized that studies with larger sample sizes, that have funding reported, that are published in journals with a higher impact factor and that are in journals that have adopted or endorsed the CONSORT statement will be associated with better overall quality of reporting and reporting of key methodological items. METHODS: We systematically reviewed RCTs used within an anesthesiology related post-operative pain management meta-analysis. We included all of the 23 RCTs used, all of which were parallel design that addressed the use of femoral nerve block in improving outcomes after total knee arthroplasty. Data abstraction was done independently by two reviewers. The two main outcomes were: 1) 15 point overall quality of reporting score (OQRS) based on the Consolidated Standards for Reporting Trials (CONSORT) and 2) 3 point key methodological item score (KMIS) based on allocation concealment, blinding and intention-to-treat analysis. RESULTS: Twenty-three RCTs were included. The median OQRS was 9.0 (Interquartile Range = 3). A multivariable regression analysis did not show any significant association between OQRS or KMIS and our four predictor variables hypothesized to improve reporting. The direction and magnitude of our results when compared to similar studies suggest that the sample size and impact factor are associated with improved key methodological item reporting. CONCLUSIONS: The quality of reporting of RCTs used within an anesthesia related meta-analysis is poor to moderate. The information gained from this study should be used by journals to register the urgency for RCTs to be clear and transparent in reporting to help make literature accessible and comparable.
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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 | 0.656 | 0.836 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.026 | 0.028 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".