Will I publish this abstract? Determining the characteristics of medical education abstracts linked to publication
Bibliographic record
Abstract
BACKGROUND: Prior studies have shown that most conference submissions fail to be published. Understanding factors that facilitate publication may be of benefit to authors. Using data from the Canadian Conference on Medical Education (CCME), our goal was to identify characteristics of conference submissions that predict the likelihood of publication with a specific focus on the utility of peer-review ratings. METHODS: Study characteristics (scholarship type, methodology, population, sites, institutions) from all oral abstracts from 2011-2015 and peer-review ratings for 2014-2015 were extracted by two raters. Publication data was obtained using online database searches. The impact of variables on publication success was analyzed using logistic regressions. RESULTS: In total, 953 oral abstracts were reviewed from 2011 to 2015. Overall, the publication rate was 30.5% (291/953). Of 531 abstracts with peer-review ratings, between 2014 and 2015, 162 (31%) were published. Of the nine analyzed variables, those associated with a greater odds of publication were: multiple vs. single institutions (odds ratio (OR) = 1.72), post-graduate research vs. others (OR=1.81) and peer-review ratings (OR=1.60). Factors with decreased odds of publication were curriculum development (OR=0.17) and innovation vs. others (OR=0.22). CONCLUSION: Similar to other studies, the publication rate of CCME presentations is low. However, peer ratings were predictive of publication success suggesting that ratings could be a useful form of feedback to authors.
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 | 0.094 | 0.588 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".