Fate of Abstracts Presented at the Saudi Ophthalmology Society Conferences 2015–2018
Bibliographic record
Abstract
PURPOSE: The aim of the study was to determine the publication rates of abstracts presented at the annual Saudi Ophthalmology Society (SOS) Conferences from 2015 to 2018. METHODS: This is a cross-sectional study on abstracts collected from the scientific programs for the SOS meetings from 2015 to 2018. Titles and first authors' names were used in the search process on PubMed. A Chi-square test was conducted to compare between the categorical variables. Kruskal–Wallis test was used for nonnormally distributed variables. RESULTS: A total of 365 abstracts were presented in the SOS Conferences from 2015 to 2018. In the SOS meetings (2015–2018), the publication rate was 45.7%. Seventy-two (43.1%) of the published abstracts were published in journals with an impact factor. The mean impact factor was 1.4 ± 1.9. The median time to publication was 12.0 months (range: 0–60 months). On univariate analysis, basic science ( P < 0.001), abstracts on rare diseases ( P = 0.003), affiliation with eye hospitals ( P < 0.001), and public hospitals (0.007) were associated with a higher publication rate. On multivariate analysis, basic science studies (odds ratio [OR]: 4.23, confidence interval [CI]: 1.77–10.12, P = 0.001), rare topic-related abstracts (OR: 2.03, CI: 1.22–3.38, P = 0.007), and eye center affiliation (OR: 1.67, CI: 1.03–2.68, P = 0.036) were associated with a better publication rate. The factors associated with publication in high impact factor journals were oral abstracts ( P = 0.007) and noncase report abstracts ( P = 0.023). CONCLUSION: Basic science studies, rare topic-related abstracts, and first author affiliation with an eye center were all associated with a higher publication rate. Orally presented and noncase report abstracts increased the chance of publication in higher impact factor journals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.000 |
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 teacher head, 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".