Factors associated with the publication and impact of CUA abstracts over the last decade
Bibliographic record
Abstract
INTRODUCTION: The Canadian Urological Association's (CUA) annual meeting is the largest gathering of Canadian urologists, and many abstracts that are presented go on to be published as peer-reviewed papers. Our objective was to determine the publication rates and impact of these abstracts, and examine predictors associated with their publication. METHODS: We identified abstracts presented at the 2010, 2013, 2014, 2015, 2018, 2020, and 2021 CUA meetings, and determined if there were matching manuscripts based on author and title using a comprehensive Medline search. Standardized data was extracted. Medians and interquartile ranges are presented, and regression models were used to determine factors associated with manuscript publication, journal impact factor, and time to publication. RESULTS: There were 1732 CUA abstracts in our years of interest. The overall publication rate was 45.4%. Median journal impact factor was 2.27 for all published abstracts and time to publication was 13.2 months. Type of presentation was significantly associated with publication rate (p<0.001), with 63.7% of podiums, 46.7% of moderated posters, and 39.5% of unmoderated posters published. The median journal impact factor was 3.45 for published podiums, 2.19 for moderated posters, and 2.10 for unmoderated posters. CONCLUSIONS: Approximately 45% of CUA annual meeting abstracts are eventually published. The type of presentation correlates well with both publication and impact factor, suggesting the CUA review process and scientific program committee does a good job of judging abstract quality.
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 | 0.032 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.023 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".