Publication Rate of Abstracts Presented at the 2011-2019 Society for Education in Anesthesia Meetings
Bibliographic record
Abstract
The Society for Education in Anesthesia (SEA) promotes dissemination of discoveries and innovations.We investigated the rate of publication of SEA Spring Meeting abstracts, hypothesizing that Research abstracts were published more frequently than Innovative Curriculum abstracts.We also studied the time between abstract presentation and publication and tracked the journals in which they were published.Methods: All abstracts presented at SEA spring meetings from 2011-2019 were included.We searched PubMed for published articles that were based on those SEA abstracts.We calculated the overall publication rate and the respective publication rates for Research and Innovative Curriculum abstracts.We calculated odds ratio (OR) and performed the Pearson χ2 test to compare publication rates between Research abstracts and Innovative Curriculum abstracts.We calculated the mean number of years between meeting presentation and publication and tabulated the number of works published in each journal.Results: A total of 351 abstracts (128 Research and 223 Curriculum) were presented at SEA spring meetings.The overall publication rate was 15% (52/351).Research abstracts were published more frequently than Curriculum abstracts: 24.2% (31/128) versus 9.4% (21/223); OR = 3.1 (95% confidence interval, 1.7-5.6);P = .0003.The mean time from presentation to publication was 1.7 ± 1.3 years.The works appeared in 20 different journals.Conclusion: SEA Spring Meeting abstracts were published less frequently than abstracts from other medical professional society meetings (21%-72.3%).Although the lower publication rate of Innovative Curriculum abstracts unique to the SEA meeting largely explains this shortfall, a relatively low publication rate, even for the Research abstracts, signals opportunities for growth.
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 | 0.025 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".