Medical terminology and interpretation of results in plain language summaries published by oncology journals.
Bibliographic record
Abstract
e18653 Background: Plain language summaries (PLS) are increasingly added to published scientific manuscripts in oncology, either as an abstract or standalone manuscript, to increase transparency in medical research. We evaluated PLS published in oncology journals by publication type and institution for criteria deemed essential to adequately inform laypeople. Methods: PLS published in oncology journals in 2021 or 2022 were identified in PubMed using prespecified search terms. Two medical writers independently reviewed and scored PLS based on use of medical terminology, language level, and adequate interpretation of data (i.e., a lay reader would be able to put the data adequately in context). Scoring discrepancies were resolved by a third reviewer. Established Flesch readability statistics were compared to the reviewers’ scoring. Scoring results were evaluated between institutions (academia or pharmaceutical companies) using Fisher's exact tests in R. Results: 63 PLS were analyzed in 9 oncology journals; scoring results are displayed in the table. All PLS manuscripts were written by pharmaceutical companies; PLS abstracts were provided by both pharmaceutical companies and academia. Only 5% of all PLS were graded as understandable at the pre-university level. Flesch readability statistics provided similar results, and reviewers’ scores correlated with Flesch readability statistics (n = 38, W = 0.599, r = 66.5, P= .002). Medical terminology was avoided or explained in all PLS manuscripts and in 23% of PLS abstracts. Similarly, data were adequately interpreted in all PLS manuscripts and 17% of PLS abstracts. PLS provided by pharmaceutical companies avoided or explained medical terminology ( P< 0.001 ) and adequately interpreted all data more often than PLS written in academia ( P< 0.001). Importantly, 6 PLS (10%) were found to overstate results. Conclusions: PLS published in oncology journals, particularly as abstracts, frequently had inadequate data interpretation and often used language only accessible for people with advanced scientific or medical training, both when written by academic investigators or within pharmaceutical companies, limiting their intended purpose. We recommend scientists in academia and pharma to adapt easily interpretable language in PLS, which would allow PLS to be used to advance equal access to healthcare research. [Table: see text]
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".