Quality and publication success of abstracts of randomized clinical trials in inflammatory bowel disease presented at Digestive Disease Week†
Bibliographic record
Abstract
BACKGROUND: The incorporation of abstracts from scientific meetings into systematic reviews and practice guidelines may reduce publication bias and delays in implementing therapeutic interventions. METHODS: All abstracts of Phase III randomized controlled trials in inflammatory bowel disease accepted at Digestive Disease Week (1998-2003) were identified. MedLine, PubMed (1997-current), EMBASE, and Google Scholar were searched for subsequent full publications. Characteristics of methodology and outcomes of the abstracts and articles were analyzed. RESULTS: The 5-year cumulative publication rate of the 82 eligible abstracts was 78%. Abstracts that presented negative results were less likely to be published than those with positive findings, particularly after the first 2 years (hazard ratio 6.45; 95% confidence interval [CI]: 2.22-18.7) with 5-year cumulative publication rates of (50% versus 91%, respectively, P < 0.001). The median time to publication was longer for negative than positive abstracts (58 versus 26 months, P < 0.001). Abstracts selected for oral presentation were more likely to be published than poster presentations (89% versus 69%; P = 0.03). A change in primary outcome results was observed in 28% (n = 18) of abstracts compared to that in the final publication, and 6% (n = 4) had a statistically significant change resulting in a change of study conclusions. CONCLUSIONS: Our findings suggest that the use of abstract data would enable detection and mitigation of publication bias. Improving the uniformity and quality of abstract reporting of randomized clinical trials at scientific meetings may facilitate their incorporation in practice guidelines and systematic reviews.
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 | 0.387 | 0.797 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".