Determinants of abstract acceptance for the Digestive Diseases Week – a cross sectional study
Bibliographic record
Abstract
BACKGROUND: The Digestive Diseases Week (DDW) is the major meeting for presentation of research in gastroenterology. The acceptance of an abstract for presentation at this meeting is the most important determinant of subsequent full publication. We wished to examine the determinants of abstract acceptance for this meeting. METHODS: A cross-sectional study was performed, based on abstracts submitted to the DDW. All 17,205 abstracts submitted from 1992 to 1995 were reviewed for acceptance, country of origin and research type (controlled clinical trials (CCT), other clinical research (OCR), basic science (BSS)). A random sub-sample (n = 1,000) was further evaluated for formal abstract quality, statistical significance of study results and sample size. RESULTS: 326 CCT, 455 OCR and 219 BSS abstracts were evaluated in detail. Abstracts from N/W Europe (OR 0.4, 95% CI 0.3-0.6), S/E Europe (OR 0.4, 95% CI 0.2-0.6) and non-Western countries (OR 0.3, 95% CI 0.2-0.5) were less likely to be accepted than North-American contributions when controlling for research type. In addition, the OR for the acceptance for studies with negative results as compared to those with positive results was 0.4 (95% CI 0.3-0.7). A high abstract quality score was also weakly associated with acceptance rates (OR 1.4, 95% CI 1.0-2.0). CONCLUSIONS: North-American contributions and reports with statistically positive results have higher acceptance rates at the AGA. Formal abstract quality was also predictive for acceptance.
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 | 0.052 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".