Contemporary trends in the level of evidence in general thoracic surgery clinical research
Bibliographic record
Abstract
OBJECTIVES: The large volume of scientific publications and the increasing emphasis on high-quality evidence for clinical decision-making present daily challenges to all clinicians, including thoracic surgeons. The objective of this study was to evaluate the contemporary trend in the level of evidence (LOE) for thoracic surgery clinical research. METHODS: All clinical research articles published between January 2010 and December 2017 in 3 major general thoracic surgery journals were reviewed. Five authors independently reviewed the abstracts of each publication and assigned a LOE to each of them using the 2011 Oxford Centre for Evidence-Based Medicine classification scheme. Data extracted from eligible abstracts included study type, study size, country of primary author and type of study designs. Three auditing processes were conducted to establish working definitions and the process was validated with a research methodologist and 2 senior thoracic surgeons. Intra-class correlation coefficient was calculated to assess inter-rater agreement. Chi-square test and Spearman correlation analysis were then used to compare the LOE between journals and by year of publication. RESULTS: Of 2028 publications reviewed and scored, 29 (1.4%) were graded level I, 75 (3.7%) were graded level II, 471 (23.2%) were graded level III, 1420 (70.2%) were graded level IV and 33 (1.6%) were graded level V (lowest level). Most publications (94.9%) were of lower-level evidence (III-V). There was an overall increasing trend in the lower LOE (P < 0.001). Inter-rater reliability was substantial with 95.5% (95%, confidence interval: 0.95-0.96) level of agreement between reviewers. CONCLUSIONS: General thoracic surgery literature consists mostly of lower LOE studies. The number of lower levels of evidence is dominating the recent publications, potentially indicating a need to increase the commitment to produce and disseminate higher-level evidence in general thoracic surgery.
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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 | 0.199 | 0.514 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.032 | 0.030 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 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".