Characterization of published errors in high-impact oncology journals
Bibliographic record
Abstract
6627 Background: Knowledge within oncology is disseminated primarily via peer-reviewed journals. The potential for dissemination of erroneous data exists, an issue that has not been explored in oncology. We evaluated errata from the Journal of Clinical Oncology and the Journal of the National Cancer Institute published between 2004–2007. Methods: Two authors independently abstracted data regarding errata and classified them as trivial (eg typographical error) or serious (eg change in outcome). For serious errors, the frequency of citation and error propagation was determined using the Science Citation Index in Web of Science. For publications cited > 150 times, a random sample of 10% were evaluated for error propagation. Canadian oncologists were surveyed regarding attitudes towards published errata. Results: There were 190 published errors, out of a total of 5118 papers, for an error rate of 4 ± 1% (SD) per year. 26/190 errors were identified as serious (14%). The median time from publication of the original article to publication of the erratum was 3.5 mo for trivial errors compared to 8.3 mo for serious errors (p = 0.03). A median of 1 error per article was reported for papers with trivial errors compared to a median of 2 errors per article with serious errors (p < 0.01). The 26 articles with serious errors were cited 256 times before publication of the error and 1056 times afterwards; of these, 96 and 527, respectively, were evaluated for propagation. Error propagation occurred in 14.6% of the citations published before error publication, and in 3.4% of citations published afterwards (p < 0.001). Survey results indicate that 30% of oncologists do not read the erratum section of journals, and that 45% of oncologists have only read the abstract of an article before citing it in a publication. Although 58% of oncologists have noticed errors in cancer publications, only 15% of these errors were reported. Conclusions: Error rates in high impact oncology journals average 4% per year, but this is likely an underestimate since errors noticed by readers are not consistently reported to the journal. The accuracy of articles submitted for publication is of utmost importance; while error propagation decreases after erratum publication, serious errors continue to be propagated in the literature. No significant financial relationships to disclose.
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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 | 0.053 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.053 | 0.048 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| 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".