Bibliographic record
Abstract
The brain enables quality of life, creativity and human progress. Without a healthy brain, little else matters. The World Federation of Neurology (WFN) aims to integrate, prioritize, and help apply advances in brain diseases and the promotion of brain health worldwide. Much has been achieved in the understanding, diagnosis and treatment of diseases of the brain in the past 50 years. However, the pace of progress needs to be accelerated further, if we are to help stem the growing burden of neurological disorders. Already, neurological disorders are the leading cause of disability adjusted life years (DALY’s) in the world, and they are projected to rise. Our agenda is huge, and our resources are modest, so that we need to integrate, focus, and leverage improvements in neurological disease and promotion of brain health worldwide. Many opportunities exist for synergy. The Greek root“syn” (with) implies working with others, both individuals and organizations. This includes national, regional, and subspecialty organizations, other international organizations dealing with the brain, the World Health Organization (WHO), governments and industry with which we can synergize. Synergy also implies that the combination is more than the sum of its parts, that is, it creates added value. Value, evaluation, and viability need to be our bywords. An important first step is to survey all relevant activities taking place in a given area. For example, a number of different organizations offer a variety of educational activities. Could something be gained by better coordination and a systematic evaluation of quality and results? It would help to know where we are, before we decide where we are going. Many more neurologists will be involved and much more will need to be done. The challenges are formidable, but so are the opportunities.
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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".