Publication outcomes for research presented at a Canadian surgical conference
Bibliographic record
Abstract
BACKGROUND: The failure of investigators to publish research in peer-reviewed journals following acceptance at a national or international meeting can lead to significant publication biases in the literature. Our objective was to evaluate the abstract to manuscript conversion rate for abstracts presented at the Canadian Society for Vascular Surgery (CSVS) annual meeting and to evaluate the conversion rate for CSVS-awarded research grants. METHODS: We searched for authors of abstracts accepted at the CSVS Annual Meeting (2007-2013) and recipients of CSVS research awards (2005-2013) on Scopus and PubMed databases to identify related publications. RESULTS: We identified 84 publications from 188 research abstracts (45%) and 17 publications from 39 research grants (44%). The mean time to publication was 1.8 years and the mean impact factor was 2.7. Studies related to endovascular therapies demonstrated a trend toward a higher rate of publication relative to open surgical therapies (64 [56%] v. 37 [27%]). Additionally, we observed a similar trend in research grant topics related to endovascular therapies relative to open surgical therapies (9 [67%] v. 8 [38%]). Finally, CSVS research grant recipients who subsequently published had a significantly higher h-index at the time of receipt than those who had not published. CONCLUSION: The CSVS annual meeting's abstract to publication conversion rate is comparable to that of its Canadian peers as well as to other medical specialties; however, a substantial publication gap remains. We identified several potential areas that may help to improve the effectiveness of CSVS research grants.
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 | 0.130 | 0.466 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.064 | 0.087 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".