Manuscript publication of abstracts presented at gynecologic surgery societies’ annual meetings
Bibliographic record
Abstract
PURPOSE: To study characteristics and identify factors associated with full manuscript publication of oral abstracts presented at gynecologic surgery societies' annual meetings. STUDY DESIGN: We reviewed all oral abstracts presented at four major gynecologic surgery meetings in 2018. Oral abstracts subsequently published as peer-reviewed manuscripts were compared to those that were not published. Descriptive statistical analysis and multivariable regression analyses were conducted to identify factors associated with peer-reviewed manuscript publication. RESULTS: A total of 396 oral presentation abstracts from the four nationally recognized gynecologic societies were identified. The overall journal publication rate was 47.4% (188/396). The rate of publication of oral abstracts was 35.1% (72/205) for those presented at AAGL, 73.8% (62/84) for AUGS, 53.2% (42/79) for SGO and 42.9% (12/28) for SGS. In multivariable regression analysis, last author's H-index [aOR 95% CI 1.02 (1.00-1.03)], academic center affiliation [aOR 95% CI 2.29 (1.20-4.37)], and randomized controlled trials [aOR 95% CI 2.47 (1.12-5.47)] were associated with journal publication. Of the published articles, the median time to publication was 3.0 years [1.0-5.0], the median journal impact factor was 3.9 [1.8-4.8], the median relative citation ratio was 1.0 [0.4-1.9], and the median number of citations per year was 2.0 [1.0-4.1]. CONCLUSIONS: In the field of gynecologic surgery, several factors, including the last researcher's H-index, academic affiliation, randomized controlled trial design and type of societal meeting are associated with increased odds of an oral abstract ultimately reaching full manuscript peer-reviewed publication. These findings can serve researchers in the fields of gynecologic surgical subspecialties.
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 | 0.031 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".