From podium to press: The 10-year publication rate of abstracts presented at the annual meetings of the Quebec Urological Association (QUA)
Bibliographic record
Abstract
INTRODUCTION: Our objective was to determine the proportion of publications arising from abstracts presented at the Quebec Urological Association (QUA). We wanted to analyze differences in publication rates according to certain parameters, and to examine the quality of publications using journal impact factors. METHODS: All abstracts presented at the annual meetings of the QUA between 2000 and 2010 were obtained from the QUA archives and searched using the PubMed database. Variables included: institute, number of abstracts presented, year of presentation and publication, impact factor of publishing journal (according to 2010 Thomson Reuters report), time to publication (months), research type, presenter and location of research. Kaplan Meier methods were used for analysis. RESULTS: By May 2012, 248 out of 439 abstracts (QUA 2000 to 2010) were published in peer-reviewed journals, resulting in a publication rate of 56%. There were significant differences in publication rates according to institution, research type and location of research. Researchers from non-Quebec institutions were twice as likely to publish compared to those from Quebec institutions (Cox HR 2.13, CI 1.20-3.76, p < 0.01). DISCUSSION: The QUA publication rate was considerably higher than previously studied by the American Urological Association (37.8%) and British Association of Urological Surgeons (≈42%); however length of follow-up and presentation types differed. Research conducted outside Quebec was more likely to be published, reflecting the multi-institution robust study designs and higher level of evidence. Factors influencing publication deserve further attention, and clinicians are encouraged to conduct research with intent to publish.
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 | 0.021 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".