A systematic review of the quality of publications reporting coronary artery bypass grafting trials.
Bibliographic record
Abstract
OBJECTIVE: Several studies have shown that the quality of reports of randomized controlled trials (RCTs) in medicine is variable and often poor, whereas the quality of those in surgery is unknown. We aimed to assess the quality of reports of RCTs in coronary artery bypass grafting (CABG) surgery when comparing off- and on-pump techniques. METHODS: From electronic searches of MEDLINE, the Cochrane Library, CINAHL, HealthSTAR and EMBASE, we identified RCTs published between 2000 and 2005 comparing off- and on-pump CABG. We assessed the report quality, using 35 items from the Consolidated Standards for Reporting Trials (CONSORT) statement and 54 additional indicators relevant to CABG surgery. Some of the indicators comprised several small parts, making the maximum possible total score 105. Two authors independently reviewed and assessed the reporting quality of each RCT. The level of agreement was assessed with kappa statistics, and disagreements were resolved by consensus. We expressed descriptive analyses as median and interquartile range; we used a generalized estimating equation (GEE) for data analysis. RESULTS: We included 50 trials, for a total of 5134 patients. The kappa value was greater than 0.6 for 73 of 105 (70%) indicators. The overall report quality score varied from 35 to 93 of 105. The CONSORT score reporting quality varied from 16 to 39 of 42. The quality of reporting was poor and insufficient for the methods (particularly, the sample size, allocation and blinding subsections), results and discussion sections. With GEE modelling, the reporting quality had a strong association with trial size, publication year, trial location and funding source, but not with the results and type of primary outcome. CONCLUSION: The quality of the publications' reporting methods, results and discussion sections was suboptimal. It is critical that, in reporting surgical trials, authors follow the CONSORT guidelines as well as consider the surgical factors.
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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 | 0.115 | 0.419 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.009 |
| Bibliometrics | 0.047 | 0.036 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".