Publication Rate of Abstracts Presented at the Annual APGO/CREOG Meeting
Bibliographic record
Abstract
PURPOSE: To determine the publication rate of abstracts presented at the Annual APGO/CREOG Meetings and compare it to rates from other medical education conferences. BACKGROUND: Abstract presentations at conferences represent an important means of disseminating scholarly activity. Failure to publish educational research in peer-reviewed journals leads to unnecessary duplication and publication bias. METHODS: The following characteristics were recorded from the 2014-2015 APGO/CREOG meeting abstracts: format (oral/poster), award status, type of scholarship (research/educational innovation), methods (quantitative/qualitative), and number of centers involved. Medline and Google Scholar were searched using the names of the first and last author, and key-words from the title and abstract. Chi-square and Fisher’s exact tests were performed to determine which characteristics were associated with publication. The previously reported publication rates from the 2005-2006 Research in Medical Education (RIME) and the Canadian Conference on Medical Education (CCME) conferences were compared to the APGO/CREOG abstracts. RESULTS: 314 abstracts were reviewed, and 29 (9%) were published. Award winning and oral abstracts, but none of the other characteristics, were associated with higher publication rates. Of the 445 abstracts reviewed from the RIME and CCME conferences 141 (31%) were published, which is a significantly higher rate than those from the APGO/CREOG meetings (p>0.05). DISCUSSION: Award winning and oral abstracts were more likely to be published. The rate of publication for abstract presented at APGO/CREOG meetings is notably lower than other conferences. APGO/CREOG should consider ways to increase the publication rate which would potentially enhance the reputation of both the presenters and the Annual Meeting.
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 | 0.058 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".