Quotation Errors in High-Impact-Factor Orthopaedic and Sports Medicine Journals
Bibliographic record
Abstract
Background: Inappropriate referencing of the existing literature has the potential to propagate false information. Quotation errors are defined as citations in which the referenced article fails to substantiate the authors’ claims. The aim of this study was to determine the rate of quotation errors in high-impact general orthopaedic and sports medicine journals and to determine whether there are article or journal-related factors that are related to the rate of inaccuracies. Methods: A total of 250 citations from the 5 orthopaedic and sports medicine journals with the highest impact factors in 2019 (per Journal Citation Reports) were chosen using a random sequence generator. Reviewers rated the chosen citations by comparing the claims made by the authors with the data and conclusions of the referenced source to determine whether quotation errors were present. Logistic regression was utilized to assess for article- and journal-related factors related to the rate of quotation errors. Results: The overall quotation error rate was 13.6%. A total of 2.8% of the claims were completely unsubstantiated. The number of quotation errors did not significantly differ between the included journals. Single citations were significantly more likely than string citations to result in citations that could not be fully substantiated (χ 2 = 4.57; odds ratio = 2.22; 95% confidence interval = 1.06 to 4.66; p = 0.03). No relationship was found between the rate of quotation errors and the total number of citations in the article, study type, or the graded level of evidence of the article. Conclusions: Quotation errors in high-impact factor orthopaedic and sports medicine journals are common. This is particularly important given the higher likelihood that studies in these journals are cited elsewhere, thus propagating the inaccuracies. Efforts from both authors and journals are needed to reduce quotation errors in the orthopaedic literature.
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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 | 0.099 | 0.539 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".