Digital Footprints, Green Impact: User Engagement Analysis of a Conference Management Platform at the 41st Annual IFMSS Meeting
Bibliographic record
Abstract
Introduction: International medical conferences face evolving challenges in optimizing scientific exchange and professional networking while minimizing their environmental footprint. Digital platforms offer solutions that can enhance engagement while reducing ecological impact. This study evaluated implementation outcomes of a custom digital platform at the 41st International Fetal Medicine & Surgery Society (IFMSS) meeting. METHODS: We conducted a prospective observational study of a custom mobile application platform deployed during IFMSS 2024 (September 22-28, 2024). The system incorporated authenticated user access, real-time session management, networking capabilities, and comprehensive analytics. Primary outcomes included user engagement metrics, scientific content interaction rates, professional networking efficacy, and platform stability. RESULTS: Platform adoption reached 91.9% (339/369 registrants), generating 178,873 discrete interactions. Scientific content engagement included 56,344 abstract/presentation views by 335 unique users. Networking features facilitated 182 new professional connections and 485 direct message exchanges. Search functionality received 4,310 targeted queries, while speaker profiles were examined 780 times. CONCLUSION: Implementation of a digital conference platform demonstrated significant efficacy in supporting conference objectives with high engagement rates. These findings suggest digital platforms can effectively enhance traditional conference structures, facilitate attendee engagement and interaction, reduce paper waste, and provide a more sustainable option for scientific exchange in subspecialty meetings. .
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".