Publication bias in gastroenterological research – a retrospective cohort study based on abstracts submitted to a scientific meeting
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to examine the determinants of publication and whether publication bias occurred in gastroenterological research. METHODS: A random sample of abstracts submitted to DDW, the major GI meeting (1992-1995) was evaluated. The publication status was determined by database searches, complemented by a mailed survey to abstract authors. Determinants of publication were examined by Cox proportional hazards model and multiple logistic regression. RESULTS: The sample included abstracts on 326 controlled clinical trials (CCT), 336 other clinical research reports (OCR), and 174 basic science studies (BSS). 392 abstracts (47%) were published as full papers. Acceptance for presentation at the meeting was a strong predictor of subsequent publication for all research types (overall, 54% vs. 34%, OR 2.3, 95% CI 1.7 to 3.1). In the multivariate analysis, multi-center status was found to predict publication (OR 2.8, 95% CI 1.6-4.9). There was no significant association between direction of study results and subsequent publication. Studies were less likely to be published in high impact journals if the results were not statistically significant (OR 0.5, 95 CI 95% 0.3-0.6). The author survey identified lack of time or interest as the main reason for failure to publish. CONCLUSIONS: Abstracts which were selected for presentation at the DDW are more likely to be followed by full publications. The statistical significance of the study results was not found to be a predictor of publication but influences the chances for high impact publication.
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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".