Characterization of the Conversion of Meeting Presentation to Publication From the 2016 and 2018 ISSVA Workshops
Bibliographic record
Abstract
Objectives: Presentations at scientific conferences and subsequent publications play a critical role in a specialty’s advancement. Previous estimates suggest that about half of the content presented at conferences is not published. The objective of this study is to characterize the conversion from meeting presentations to publications from the 2016 and 2018 International Society for the Study of Vascular Anomalies (ISSVA) Workshops. Methods: The PubMed interface (MEDLINE) and Google Scholar were used to search for published works. Excluded presentations included keynotes, research letters, and education-related theses. Parameters reviewed included conversion rate, time to publication, senior author subspecialty, study design and level of evidence, journal name, and 5-year impact factor. Results: 40.43% of searched conference abstracts were published in peer-reviewed journals. The median publication time from presentation was 16 months (range −28.9 to 41.1). The most frequent specialties of 224 senior authors were: plastic surgery (21.4%), dermatology (20.0%), and radiology (10.7%). Authors published in 111 separate journals, where the majority of publications appeared in Pediatric Dermatology (5.8%). A majority of publications (51.3%) had a case series study design and were level 4 evidence. The median 5-year impact factor was 3.49. Conclusions: From the 2016 and 2018 ISSVA meetings reviewed, less than half of presentations were converted to publications. Studies were published in a wide range of journals, in alignment with specialty. A significant portion of vascular anomalies research at ISSVA that may have the potential to improve patient care does not reach a wider audience.
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 | 0.033 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".