Assessment of antinuclear antibodies by indirect immunofluorescence assay: report from a survey by the American Association of Medical Laboratory Immunologists
Bibliographic record
Abstract
Background: The indirect immunofluorescence assay (IFA) using HEp-2 cell substrates is the preferred method by some for detecting antinuclear antibodies (ANA) as it demonstrates a number of characteristic staining patterns that reflect the cellular components bound as well as semi-quantitative results. Lack of harmonized nomenclature for HEp-2 IFA patterns, subjectivity in interpretation and variability in the number of patterns reported by different laboratories pose significant harmonization challenges. The main objectives of this study were to assess current practice in laboratory assessment of HEp-2 IFA, identify gaps and define strategies to improve reading, interpretation and reporting. Methods: We developed and administered a 24-item survey based on four domains: educational and professional background of participants, current practice of HEp-2 IFA testing and training, gap assessment and the perceived value of International Consensus on Antinuclear Antibody Patterns (ICAP) and other factors in HEp-2 IFA assessment. The Association of Medical Laboratory Immunologists (AMLI) and American Society for Clinical Pathology administered the survey from April 1 to June 30, 2018, to members involved in ANA testing. This report summarizes the survey results and discussion from a dry workshop held during the 2019 AMLI annual meeting. Results: One hundred and seventy-nine (n = 179) responses were obtained where a significant number were clinical laboratory scientists (46%), laboratory directors (24%), supervisors (13%) or others (17%). A majority of respondents agreed on the need to standardize nomenclature and reporting of HEp-2 IFA results. About 55% were aware of the ICAP initiative; however, among those aware, a significant majority thought its guidance on HEp-2 IFA nomenclature and reporting is of value to clinical laboratories. To improve ICAP awareness and further enhance HEp-2 IFA assessment, increased collaboration between ICAP and the clinical laboratory community was suggested with emphasis on education and availability of reference materials. Conclusions: Based on these suggestions, future efforts to optimize HEp-2 IFA reading, interpretation and reporting would benefit from more hands-on training of laboratory personnel as well as continuous collaboration between professional organizations, in vitro diagnostic manufacturers and clinical laboratories.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".