The Impact of SARS-CoV-2 (COVID-19) Pandemic on International Dermatology Conferences in 2020
Bibliographic record
Abstract
To limit the spread of the SARS-CoV-2 (COVID-19) outbreak, humans have been significantly restricted in their ability to travel and interact with others worldwide. Consequently, dermatology conferences were forced to adapt to such changes. The aim of this study is to investigate the impact of COVID-19 on international dermatology conferences. We retrospectively investigated decisions made for international dermatology conferences scheduled for 2020. Thirty-three major conferences were analyzed. Their data were obtained from their respective websites (data was accessed 2 June 2021). Among 33 conferences analyzed, 13 (39.4%) were conducted as scheduled, nine (27.3%) were canceled, eight (24.3%) were postponed to 2021 or 2022, and three (9.1%) were delayed but conducted in 2020. The number of the cancellation (44.4%) and postponement (75%) was the largest in the second quarter of the year. During the fourth quarter, most conferences were held on schedule (70%) but were run virtually. Eight out of 13 virtual conferences shortened their duration (61.5%). Most (90.9%) conferences have decided on the schedule of their meetings for 2021 or 2022 while three (9.1%) remain undecided. Twelve (40%) are planned to run virtually, eight (26.7%) have opted for a hybrid form, five (16.7%) are planned to run in-person, four (13.3%) have not decided on the format, and one (3.3%) has been canceled. Virtual and hybrid conference formats have facilitated people to share knowledge despite the travel restrictions posed by the COVID-19 pandemic. Such formats are environmentally friendly, are able to attract a large audience, and save delegates time and costs involved in attending. Therefore, virtual platforms should continue to be integrated within conferences in the post-pandemic era.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".