Conversion Rate of Abstracts Presented at the Societe Internationale d’Urologie into Peer-Reviewed Journal Publications
Bibliographic record
Abstract
ObjectivesThe objective of this study was to determine the publication rate of abstracts presented at the Société Internationale d’Urologie (SIU) Congress and to analyse the characteristics associated with conversion to publication.MethodsAll abstracts from the 36th Congress of the Société Internationale d’Urologie were identified from the published 2016 abstract book. A PubMed search was performed using key words and author names to identify published journal articles corresponding with the presented abstracts.ResultsThe conversion rate of presented abstracts to publication by April 2022 was 30.73% (224 of 729). Many abstracts were published prior to presentation (35.27%, 79 of 224). The average time to publication of abstracts published post presentation was 16.88 months. The majority of abstracts were presented in urology-specific journals (66.96%, 150 of 224). Publishing journals had an average impact factor of 3.068 with Urology (18 of 224) and Worl d Journal of Urology (8 of 224) being the most common journals. Moderated ePosters had the highest conversion rate to publication (39.59%), whilst Unmoderated Videos had the lowest (11.32%). The abstract book assigned presentation topic groups to the moderated ePoster category; the most published abstract topic was sexual function (68.75%, 11 of 16).ConclusionsThe conversion rate of abstracts presented at the SIU to publications in peer-reviewed journals has shown improvement since previous reports; however, it remains lower than the rates associated with other major urological conferences. Almost 70% of presented abstracts do not convert to publication and this should be considered when incorporating abstract findings into clinical practice.
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 | 0.023 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".