Report of the First International Consensus on Standardized Nomenclature of Antinuclear Antibody HEp-2 Cell Patterns 2014–2015
Bibliographic record
Abstract
During the 12th International Workshop on Autoantibodies and Autoimmunity (IWAA) held in Sao Paulo, Brazil, on August 28, 2014, a full day session was devoted to establishing a consensus on the nomenclature of staining patterns observed in the antinuclear antibody (ANA) indirect immunofluorescence test on HEp-2 cells. The current report summarizes the collective agreements with input from the host Brazilian and international communities that represented research, clinical, and diagnostic service laboratories. Patterns are categorized in three major groups (nuclear, cytoplasmic, and mitotic patterns) and each pattern has been defined and described in detail. The consensus nomenclature and representative patterns are made available online at the International Consensus on Antinuclear antibody Pattern (ICAP) website (www.ANApatterns.org). To facilitate continuous improvement and input, specific comments on ICAP are encouraged and these will be discussed in subsequent ICAP meetings. The ultimate goal with the establishment of the ICAP is to promote harmonization and understanding of autoantibody test nomenclature, as well as interpretation guidelines for ANA testing, thereby optimizing usage in patient care.
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 | 0.096 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".