Quality and content of abstracts in papers reporting about drug exposures during pregnancy
Bibliographic record
Abstract
BACKGROUND: Most clinicians read only the abstract of papers in scientific journals. Therefore, it is very important that abstracts contain as much information as possible, to summarize the data succinctly. Our objectives were to evaluate the quality of information in abstracts reporting human fetal outcomes following drug exposure during pregnancy. METHODS: We developed quality criteria based on previous work, modifying them for use with pregnancy outcomes. Quality scores were calculated as present/absent for all of the equally weighted criteria, then expressed as percentages (present/[present + absent]). We examined a random sample of 100 abstracts obtained through searches of MEDLINE, EMBASE, and the Web of Science databases from 1990 to 2005. Average quality scores were compared across designs (cohort, case-control, meta-analysis, and mixed design) Using Kruskal-Wallis ANOVA and structured/unstructured formats using Student's t test. RESULTS: The overall average quality was 59.2% +/- 14% (median, 61.5%; range, 15.4-83.3%). Quality was not significantly different across designs (P = .16) or between structured and unstructured abstracts (P = .44). Quality scores increased over time (Rho = 0.23, P = .02). Most frequently absent were baseline risk (94%), drug dose (91%), nonsignificant P values (72%), confounders (69%), significant P values (57%), and risk difference (48%). CONCLUSIONS: Abstracts provide insufficient information, particularly baseline risk values, for readers to make evidence-based decisions regarding drug use during pregnancy. Efforts need to be made to improve the quality of abstracts and include critical information such as baseline risk.
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 | 0.328 | 0.765 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.061 | 0.048 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".