Reporting of abstracts in studies that used routinely collected data for exploring drug treatment effects: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: In recent years, studies that used routinely collected data (RCD), such as electronic medical records and administrative claims, for exploring drug treatment effects, including effectiveness and safety, have been increasingly published. Abstracts of such studies represent a highly attended source for busy clinicians or policy-makers, and are important for indexing by literature database. If less clearly presented, they may mislead decisions or indexing. We thus conducted a cross-sectional survey to systematically examine how the abstracts of such studies were reported. METHODS: We searched PubMed to identify all observational studies published in 2018 that used RCD for assessing drug treatment effects. Teams of methods-trained collected data from eligible studies using pilot-tested, standardized forms that were developed and expanded from "The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology" (RECORD-PE) statement. We used descriptive analyses to examine how authors reported data source, study design, data analysis, and interpretation of findings. RESULTS: A total of 222 studies were included, of which 118 (53.2%) reported type of database used, 17 (7.7%) clearly reported database linkage, and 140 (63.1%) reported coverage of data source. Only 44 (19.8%) studies stated a predefined hypothesis, 127 (57.2%) reported study design, 140 (63.1%) reported statistical models used, 142 (77.6%) reported adjusted estimates, 33 (14.9%) mentioned sensitivity analyses, and 39 (17.6%) made a strong claim about treatment effect. Studies published in top 5 general medicine journals were more likely to report the name of data source (94.7% vs. 67.0%) and study design (100% vs. 53.2%) than those in other journals. CONCLUSIONS: The under-reporting of key methodological features in abstracts of RCD studies was common, which would substantially compromise the indexing of this type of literature and prevent the effective use of study findings. Substantial efforts to improve the reporting of abstracts in these studies are highly warranted.
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 | 0.368 | 0.653 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.036 | 0.032 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".