Collaterals 2016: Translating the collaterome around the globe
Bibliographic record
Abstract
Collaterals 2016 (third International Symposium on Collaterals to the Brain) was a multidisciplinary scientific conference focused on collateral circulation in acute ischemic stroke. Decisive challenges include generalizability of optimal triage and selection paradigms based on collateral status for definitive treatment of acute ischemic stroke, rapid dissemination of expert methods, and the urgent need to leverage networking opportunities for stroke science related to the hemodynamics of collaterals. The collaterome, or individual capacity to offset ischemia in the brain, and determination of a favorable collateral profile have become pivotal factors in consideration of the precision medicine of stroke decision-making. The conference convened over 50 invited faculty from around the world to connect on-site participants at a state-of-the-art facility with remote audiences in more than 22 countries and regions. The 2½-day program was structured into 40-min sessions devoted to key issues in translating the collaterome in acute stroke therapy across the globe. This unique forum of expertise emphasized the timely impact of collaterals on a monumental scale, encouraging maximal participation, rapid diffusion and added value of a diverse networking resource. The meeting format established a model geographical framework and innovative videoconferencing platform for future scientific conferences.
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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
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".