International science at the annual meeting of the American Academy of Neurology
Bibliographic record
Abstract
The annual meeting of the American Academy of Neurology (AAN) is a major venue for presentation of the latest disease-related clinical and basic neurologic research and is attended by a large number of neurologists from countries outside the United States and Canada. One-third of annual meeting attendees and abstract submissions are international in origin, with wide variations between countries and world regions, and this proportion has remained stable for the past 5 years. By comparison, international neurologists constitute 12% (n = 2,485) of AAN membership, and international membership has declined slightly over the past 5 years compared to a 15% increase from the United States and Canada. The scientific topics covered by international abstracts are similar to those from the United States and Canada. Abstract acceptance rates are 15% lower for international submissions than for those from the United States and Canada although variations between countries are seen. Three times more European neurologists attend the annual meeting than are AAN members whereas Asian neurologists are more likely to be AAN members than to attend the annual meeting. The AAN is working to understand and address the issues that affect international physicians' decisions to participate in 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.168 | 0.083 |
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".