Has the quality of reporting of randomized controlled trials in thoracic surgery improved?
Bibliographic record
Abstract
OBJECTIVES: To evaluate the quality of reporting of randomized controlled trials (RCTs) in the thoracic surgery literature according to Consolidated Standard for Reporting of Trials (CONSORT) and to determine predictors of quality. METHODS: All RCTs published in four principal journals between 1998 and 2013 were identified in PubMed. Two independent reviewers assessed each trial using the CONSORT checklist (1996) with discrepancies resolved by a third reviewer. Mean checklist scores were compared between trials published from 1998 to 2005 and 2006 to 2013. The κ statistic for inter-rater agreement was calculated. Stepwise multivariable linear regression was then performed to identify independent predictors of quality. RESULTS: After 2 rounds of review, 203 of the 2838 identified articles met inclusion criteria. The overall κ coefficient was 0.95 indicating very good agreement between reviewers. The mean CONSORT score was significantly higher in 2006-13 [mean 10.8; 95% confidence interval (CI): 10.3-11.2] than in 1998-2005 (mean 9.3; 95% CI: 8.7-9.6). On multivariable analysis, there was strong evidence of an increased mean CONSORT score in studies comparing non-surgical interventions, multicentre trials, publications after 2006, studies with increased number of authors and studies funded by industries. CONCLUSIONS: Our study suggests that the quality of reporting in the thoracic surgery literature is improving with time and is predicted by factors including number of authors, multicentre trials, type of comparison, time period of publication and industry sponsorship. Ongoing efforts should be made to improve the quality of reporting in thoracic surgery.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.978 | 0.926 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.057 | 0.036 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".