What is the ultimate fate of presented abstracts? The conversion rates of presentations to publications over a five-year period from three North American plastic surgery meetings
Bibliographic record
Abstract
BACKGROUND: Advancements in clinical decision-making are influenced by presentations made at scientific conferences or publications in journals with extensive readership. However, many ideas shared at annual conferences fail to be published, and most surgeons attend these meetings only sporadically. OBJECTIVE: To quantify the conversion rates of meeting presentations to publications in North American plastic surgery. METHODS: MEDLINE (OvidSP) and PubMed databases were cross-referenced with abstracts accepted for podium presentation at the Canadian Society of Plastic Surgeons, American Society of Plastic Surgeons, and American Association of Plastic Surgeons annual meetings from 2003 to 2007. Parameters reviewed included publication rate, time to publication, subspecialty, trial type, publication journal and journal impact factor. RESULTS: Over the five-year study period, 45.00% of the 888 presentations were published in peer-reviewed journals. The mean time to publication was 22 months (range 1.00 to 85.90 months). In total, 57.00% of the 400 publications appeared in Plastic and Reconstructive Surgery; 47.20% of publications were case series study design. The majority of publications were of the reconstruction subspecialty (31.00%). Abstracts from the American Society of Plastic Surgeons had the highest conversion rate (57.70%). Publications based on abstracts presented at the American Association of Plastic Surgeons had the highest mean journal impact factor (2.33). The Canadian Society of Plastic Surgeons had the highest total number of publications (n=161). CONCLUSIONS: From the three North American annual general meetings reviewed, there was a modest conversion rate of mainly reconstructive case series published predominantly in a single journal, Plastic and Reconstructive Surgery. Several years often pass from the genesis of a research hypothesis to final publication, and because the majority of presentations fail to be published, presentations should be observed with a critical eye given the more stringent peer review process and time required for final publication. In an effort to improve conversion rates, departments and faculty members must foster a culture that prioritizes publication.
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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 | 0.066 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".