The Quality of Randomized Controlled Trials in Major Anesthesiology Journals
Bibliographic record
Abstract
In Brief Increased attention has been directed at the quality of randomized controlled trials (RCTs) and how they are being reported. We examined leading anesthesiology journals to identify if there were specific areas for improvement in the design and analysis of published clinical studies. All RCTs that appeared between January 2000 and December 2000 in leading anesthesiology journals (Anesthesiology,Anesthesia & Analgesia,Anaesthesia, and Canadian Journal of Anaesthesia) were retrieved by a MEDLINE search. We used a previously validated assessment tool, including 14 items associated with study quality, to determine a quality score for each article. The overall mean weighted quality score was 44% ± 16%. Overall average scores were relatively high for appropriate controls (77% ± 7%) and discussions of side effects (67% ± 6%). Scores were very low for randomization blinding (5% ± 2%), blinding observers to results (1% ± 1%), and post-beta estimates (16% ± 13%). Important pretreatment clinical predictors were absent in 32% of all studies. Significant improvement in the reporting and conduct of RCTs is required and should focus on randomization methodology, the blinding of investigators, and sample size estimates. Repeat assessments of the literature may improve the adoption of guidelines for the improvement of the quality of randomized controlled trials. IMPLICATIONS: The quality of reporting of randomized controlled trials in general anesthesiology journals may be enhanced by clarification of study methods. Randomization methods and blinding of investigators who collect data from treatment strategies are among the areas requiring the most attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.602 | 0.890 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.050 | 0.053 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| 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".