Can we Trust the References? The Challenge of Misinformation in Medical Research
Bibliographic record
Abstract
Is the information on which we base our clinical decisions and research agendas reliable? References form the foundation of our published knowledge base. Are they properly cited? Do they truly provide the support claimed? A recently published study1 suggests that 16.6% of the references in scientific journals are quoted in error—in other words, they do not support the statements for which they are cited. To arrive at this conclusion, the authors canvassed the authors of some 765 380 citations in 263 610 online publications from 5 publishers of 670 scientific journals between 2018 and 2020. Authors, representing 72 countries (the United States, 20.8% and the United Kingdom, 11.2%, the most common), were asked numerous questions regarding a citation of one of their articles with a particular emphasis on the extent to which the author’s cited article supported the statement(s) attributed to it by the citing article. Interestingly and uniquely enough authors were also asked general questions about their experiences with inappropriate citation of their work, as well as any attempted actions that they had taken when encountering such inappropriate citations. Although representation was heaviest from the life (45.1%) and health sciences (35.5%), there were a broad array of fields surveyed with general consistency across the disciplines—13.1% error rate for the physical and environmental sciences and 20.4% in engineering, technology, and applied sciences, with health (18.2%) and life (15.4%) sciences falling in between, without statistical differentiation. Although various dimensions of the problem were explored—the citing article oversimplified the cited article, the former inappropriately generalized the latter, the citation was misleading, the citation was surprising—the key parameter assessed was “I disagree” (10.1%) or “I strongly disagree”(6.5%) that this was an “appropriate citation of my article.” Of note, 46.4% of respondents said that they had previously encountered inappropriate citation of their work, while 9.3% had contacted the authors of the citing article, 3.1% the journal editor and 0.7% the publisher. The use of the judgement of the cited author as the arbiter rather than an external and/or expert reviewer was unusual but not unique in this field.2
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.286 | 0.690 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.031 | 0.061 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.026 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".