Report on the second International Consensus on ANA Pattern (ICAP) workshop in Dresden 2015
Bibliographic record
Abstract
The second meeting for the International Consensus on Antinuclear antibody (ANA) Pattern (ICAP) was held on 22 September 2015, one day prior to the opening of the 12th Dresden Symposium on Autoantibodies in Dresden, Germany. The ultimate goal of ICAP is to promote harmonization and understanding of autoantibody nomenclature, and thereby optimizing ANA usage in patient care. The newly developed ICAP website www.ANApatterns.org was introduced to the more than 50 participants. This was followed by several presentations and discussions focusing on key issues including the two-tier classification of ANA patterns into competent-level versus expert-level, the consideration of how to report composite versus mixed ANA patterns, and the necessity for developing a consensus on how ANA results should be reported. The need to establish on-line training modules to help users gain competency in identifying ANA patterns was discussed as a future addition to the website. To advance the ICAP goal of promoting wider international participation, it was agreed that there should be a consolidated plan to translate consensus documents into other languages by recruiting help from members of the respective communities.
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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.013 |
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".