The Journal Impact Factor of Orthopaedic Journals Does not Predict Individual Paper Citation Rate
Bibliographic record
Abstract
BACKGROUND: The journal impact factor (JIF) is thought to reflect the average number of citations an article will receive and therefore can influence study impact and clinical decision making. However, analysis of citation rates across multiple scientific and research domains has shown that most articles will not reach this expected number of citations. This phenomenon is known as citation skew and it has not previously been examined in the orthopaedic literature. The objective of this study was to determine the extent to which citation skew exists within orthopaedic journals and thus to determine whether the JIF in the orthopaedic literature reflects individual study citation rates. METHODS: We used data from the Thomson Reuters (now Clarivate Analytics) Web of Science to determine the 2015 JIF and citation distribution for all orthopaedic journals listed in the database. We calculated the percentage of articles with fewer citations than the JIF for each journal. Finally, we analyzed the citation distribution within groups of orthopaedic subspecialty publications. RESULTS: We identified a total of 74 orthopaedic journals and 29,296 publications for the years 2013 and 2014. Across all orthopaedic journals, 85% of published articles are cited fewer times than the JIF would indicate. The median number of citations of all articles was zero for all journals (interquartile range = 0-0) except for seven journals, for which the median number of citations per article was 1. CONCLUSION: Citation skew is prevalent across the orthopaedic literature. Most published work is not cited in the first 2 years following publication, and the JIFs are the result of a few highly cited articles. The assessment of an individual orthopaedic study's quality should not be determined by the JIF but rather by direct evaluation of the methodology, relevance, and appropriateness of the study's conclusions.
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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 | 0.026 | 0.267 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".