Widespread Use of Misidentified Cell Line KB (HeLa): Incorrect Attribution and Its Impact Revealed through Mining the Scientific Literature
Bibliographic record
Abstract
Abstract Continuous cell lines are widely used, but can result in invalid, irreproducible research data. Cell line misidentification is a common problem that can be detected by authentication testing; however, misidentified cell lines continue to be used in publications. Here we explore the impact of one misidentified cell line, KB (HeLa), on the scientific literature. We identified 574 articles between 2000 and 2014 that provided an incorrect attribution for KB, in accordance with its false identity as oral epidermoid carcinoma, but only 57 articles that provided a correct attribution for KB, as HeLa or cervical adenocarcinoma. Statistical analysis of 57 correct and 171 incorrect articles showed that the number of citations to these articles increased over time. Content analysis of 200 citing articles showed there was a tendency to describe the cell line in accordance with the description in the cited paper. Analysis of journal impact factor showed no significant difference between correct and incorrect groups. Articles using KB or citing that usage were most frequently published in the subject areas of pharmacology, pharmacy, oncology, and medicinal chemistry. These findings are important for science policy and support the need for journals to require authentication testing as a condition of publication. Cancer Res; 77(11); 2784–8. ©2017 AACR.
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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 | 0.032 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.051 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".