Levels of evidence ratings in the urological literature: an assessment of interobserver agreement
Bibliographic record
Abstract
OBJECTIVE: To determine to what extent urologists with no specific training agree upon level of evidence (LoE) ratings of studies published in the urological literature, as LoE are commonly referenced as a measure of evidence quality. MATERIALS AND METHODS: In all, 86 clinical research studies published in four major urology journals were reviewed. Each article was independently reviewed by eight reviewers using a standardized data abstraction form. Articles were assessed for type of study (therapy, prognosis, diagnosis or economic) and LoE (I, II, III or IV). Reviewers received only written instructions and no formal training in the application of this classification system. RESULTS: Of the 86 articles, 69% related to therapy, 16% to prognosis, and 15% to diagnosis. Eight studies (9%) provided Level I evidence, 18 studies (21%) Level II, 14 studies (16%) Level III and 46 studies (54%) Level IV evidence. The intraclass correlation coefficient (95% confidence interval) based on all reviewers (eight reviewers) was 0.67 (0.59-0.74; P= 0.001) for the type of study and 0.55 (0.48-0.64; P= 0.001) for the LoE. In an analysis limited to a subset of studies in which all reviewers agreed upon the type of study question (n= 40) the intraclass correlation coefficient was 0.79 (0.70-0.86; P= 0.001). CONCLUSION: In the present study there was a low interobserver agreement for LoE ratings by urologists with no specific training. These findings suggest caution in the interpretation of LoE ratings and emphasize the importance of specific training for individuals that are charged with quality of evidence determinations.
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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 | 0.422 | 0.687 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.042 | 0.021 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.002 |
| 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".