Evaluating the effectiveness of citation count as a measure of methodological quality in esophagogastric surgery research: a comparative analysis with the MINORS score and levels of evidence.
Bibliographic record
Abstract
Aim: The primary objective was to assess the relationship between the citation number and the quality of the articles, as compared with the level of evidence and the MINORS score. This study's secondary objective was to characterize the 50 most cited articles in the field of oesophagectomy research. Background: There has been an increased need for an evaluation tool to indicate research quality. Available quality assessment tools include the Level of Evidence, the MINORS score, the Cochrane Risk of Bias 2.0 Tool, the Newcastle Ottawa Scale, CASP Appraisal Checklists, and Legend Evidence Evaluation tools. Methods: The Web of Science allowed evaluating and comparing articles on oesophagectomy research. The quality of the 50 most cited articles was assessed using the Oxford Centre level of evidence classification and the methodological index for non-randomized studies (MINORS). Results: Level of evidence II studies were cited more than level IV (P=0.008). There was a significant positive correlation between citation number and MINORS score (P=0.002). The median MINORS score was highest amongst level II studies, followed by levels III, IV, and I. The median MINORS score for level II evidence was significantly higher than for level IV (P=0.001). The study sample size is associated with higher levels of evidence but does not correlate with the citation number. Female authors contributed to 4 out of 50 articles. Recently published articles tended to be cited more frequently. More authors equated to more citations. Prospective studies are more likely to be cited. Conclusion: Citation analysis can be used as an indicator of quality when assessing articles. It should, however, be used with caution as highly cited work, famous authors, and journals are all more likely to be cited. Citation analysis should be used alongside other well-established tools.
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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 | 0.325 | 0.741 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.073 | 0.067 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".